1 Department of Biology, Mount Allison University, 53 York St., Sackville NB, Canada, E4L 1C9.
2 Faculty of Veterinary Medicine, University of Calgary, 3280 Hospital Dr NW, Calgary, AB, T2N 4Z6.
3 Faculty of Computer Science, Dalhousie University, 6050 University Ave, Halifax, NS, B3H 1W5.
4 Center Algatech, Laboratory of Photosynthesis, Novohradska 237, Trebon, CZ 37981, Czech Republic.

Correspondence: Douglas A. Campbell <>

XXX Cite this page for logit2prob https://sebastiansauer.github.io/convert_logit2prob/

Abstract

Marine phytoplankton produce and scavenge Reactive Oxygen Species, to support cellular processes, while limiting damaging reactions. Some prokaryotic picophytoplankton have, however, lost all genes encoding scavenging of hydrogen peroxide. Such losses of metabolic function can only apply to Reactive Oxygen Species which potentially traverse the cell membrane, before provoking damaging intracellular reactions. We therefore hypothesized that cell radius influences which elements of Reactive Oxygen Species metabolism are potentially dispensable from a cell. We investigated genomes and transcriptomes from diverse marine eukaryotic phytoplankton, ranging from 0.4 to 44 µm radius, to analyze the genomic allocations encoding enzymes metabolizing Reactive Oxygen Species. Superoxide has high reactivity, short lifetimes and limited membrane permeability. Genes encoding superoxide scavenging were ubiquitous, but the fractional gene allocation decreased with increasing cell radius, consistent with a nearly fixed set of core genes for scavenging superoxide pools. Hydrogen peroxide has lower reactivity, longer intracellular and extracellular lifetimes and readily crosses cell membranes. Genomic allocations to hydrogen peroxide production decrease with increasing cell radius, more than do genomic allocations to scavenging of hydrogen peroxide, consistent with maintenance of ROS homeostasis in the face of slower diffusional losses of hydrogen peroxide from larger cells. Gene allocations to hydrogen peroxide metabolism are positively influenced by capacity for colony formation, suggesting more active hydrogen peroxide metabolism among the closely spaced cells of colonies, compared to single cells; and by the presence of flagella, suggesting an interaction between flagella and maintenance of Reactive Oxygen Species homeostasis. Nitric Oxide has low reactivity, long intracellular and extracellular lifetimes and readily crosses cell membranes. Neither Nitric Oxide production nor scavenging genomic capacities changed with increasing cell radius. Centric vs. pennate diatoms, however, show contrasting gene allocations to nitric oxide metabolism, consistent with diverse roles of nitric oxide across taxa.

Introduction

Phytoplankton cells span a large size range, from picoplankton (<2µm), nanoplankton (2 to 20µm), microplankton (20 to 200µm) to macroplankton (200 to <2000µm) [1]. Cell size interacts with multiple selective pressures, including cellular metabolic rate, light absorption, nutrient uptake, cell nutrient quotas, trophic interactions and diffusional exchanges with the environment [1–5]. Beyond simple size, cells of different shapes differ in surface area to volume ratio. For example, more elongated cells, such as pennate diatoms, have a larger surface area to volume ratio compared to more rounded cells, such as centric diatoms, of equivalent biovolume [6], which can in turn influence diffusional exchanges between cells and their environment.

Characteristics of Reactive Oxygen Species

Phytoplankton both produce and scavenge Reactive Oxygen Species (ROS), both within and outside the cell membrane, enzymatically and non-enzymatically. Some ROS readily cross the cell membrane, connecting intra- and extra-cellular pools. Other ROS rarely cross cell membranes and therefore intra- and extra-cellular pools are at least partially segregated (Table 1).

Table 1: Diffusion and Stability of Different ROS
ROS ROSSymbol SeawaterLow_M SeawaterHigh_M ConcentrationCitation DiffusionDistance_nm DiffusionDistanceCitation LifetimeLow_s LifetimeHigh_s LifetimeCitation CrossesCellMembrane CrossesCellMembraneCitation AbioticProductionLow_Ms-1 AbioticProductionHigh_Ms-1 AbioticProductionRatesCitation DiffCoef_um2us DiffCoefCitation
Hydrogen Peroxide H2O2 1e-09 1e-06 [7,8] NA NA 3.6e+03 1.2e+05 [7–9] Yes [10–13] 1e-13 1e-11 [14] 1500 [15]
Superoxide O2•− 1e-12 1e-09 [7,16] 320 [9] 1.0e-03 6.0e+01 [7,16] No [17] 1e-14 1e-10 [14] 210 [18]
Nitric Oxide NO 1e-12 1e-10 [16,19] NA NA 2.0e+00 2.0e+01 [16,20] Yes [21] 1e-13 1e-11 [16] 2210 [22]
Hydroxyl Radical HO 1e-18 1e-15 [7] 4.5 [9] 1.0e-06 1.0e-06 [7] No [23] 3.055556e-22 2.777778e-12 [24] NA NA
Singlet Oxygen 1O2 1e-13 1e-14 [25] 82 [9] 1.0e-06 1.0e-06 [9] NA NA NA NA NA 2100 [26]
Peroxynitrite ONOO 1e-12 1e-11 [16] NA NA 1.0e-03 1.0e-03 [16,27] Yes [28] 1e-11 1e-09 [16] NA NA

Superoxide (O2•−), a radical anion generated through the monovalent reduction of O2 to O2•− [29], is present in the oceans at concentrations of 10-12 to 10-9 [7,16]. O2•− is highly reactive [30] with organic compounds including thiols [31] and with metals [32,33]. As the first ROS in a sequential series of reductions of O2, O2•− is a ‘gateway’ to production of other ROS. O2•− is produced both inside and outside a cell, but shows limited diffusion across the hydrophobic cell membrane [17]. Electron transfers to O2 from the photosynthetic and mitochondrial electron transport chains generate intracellular O2•− as a by-product [34–39]. Multiple oxidases (Table 3) reduce O2 and generate either H2O [40], or alternately O2•− and/or Hydrogen Peroxide (H2O2) [41,42]. Biogenic production of extracellular ROS mediated by taxa ranging from heterotrophic bacteria to diatoms is significant in marine environments [7,43–50]. Increasing cell suspension density leads to decreased O2•− production per cell [48,51,52]. Some extracellular O2•− production likely contributes to cell growth, in that the removal of extracellular ROS from cultures of the heterotrophic bacteria Roseobacter inhibits its growth [51]. O2•− in coastal waters is primarily attributable to extracellular production mediated by eukaryotic phytoplankton [53]. In contrast, Prochlorococcus MED4 shows the least extracellular production of O2•− among microbes analyzed to date [48]. O2•− production patterns can vary even within a genus [47].

Two known enzymes mediate conversion of O2•− to H2O2; the ubiquitous dismutation of O2•− catalyzed by diverse Superoxide Dismutases (SOD) or the less prevalent reduction of O2•−, catalyzed by Superoxide Reductase (SOR) at the expense of metabolic reductant. O2•− also dismutates spontaneously to produce H2O2 and O2 [54], although [55] found that ~52% of dark O2•− production instead likely undergoes oxidation back to O2. Extracellular production of O2•− thus indirectly contributes to extracellular H2O2 pools [47,48,56,57].

H2O2 is an uncharged compound, present in the ocean at concentrations of 10-9 to 10-6 mol L-1 [7], depending upon location and conditions including temperature, light level, depth of the mixed layer and the concentration of total dissolved organic carbon [58]. In aquatic systems, H2O2 has lifetimes of hours to days (Table 1) [7,9]. H2O2 passively traverses cell membranes [59], primarily through aquaporins [10–13], allowing exchange of intracellular and extracellular pools of H2O2, although cells can maintain a concentration gradient between internal and external H2O2 [60].

H2O2 is acutely toxic to most cells in the range of 10-5 to 10-4 mol L-1 [59] (Table 1). H2O2 can react with thiols and methionine [31] and can interfere with gene expression [61]. Cytotoxic effects of H2O2, including lipid damage, are however, primarily caused by H2O2 dismutating into the hydroxyl radical, which is strongly oxidative [9].

Multiple different oxidases are important in producing H2O2 including glycolate oxidase, NADPH oxidase, oxalate oxidase, amine oxidase [42] and many others (Table 3), but abiotic processes, including rainfall, may be dominant sources of extracellular H2O2 in seawater [62–64]. H2O2 concentrations in seawater follow a diurnal cycle with a peak at mid-day [58,62,65], suggesting significant direct or indirect photochemical or photobiological generation of H2O2. Heterotrophs do not contribute much of the H2O2 production but significantly mediate H2O2 detoxification [66]. H2O2 also decomposes spontaneously, though slowly, into water and oxygen [67], and contributes significantly to the redox cycling of copper and iron in seawater [68,69].

Prochlorococcus and some strains of Synechococcus lack all genes encoding enzymes for scavenging of H2O2, and are therefore difficult to grow in axenic cultures, particularly at higher cell densities. Growth and survival of these strains improves when co-cultured with other ‘helper’ bacteria which carry genes for catalase [70–73]. These culture results were found at Prochlorococcus cell densities somewhat higher than typical ocean levels of 1 × 105 to 3 × 105 Prochlorococcus cells ml-1 [74], where auto-intoxication from metabolism of the Prochlorococcus cell population might be less prevalent [75]. In the open ocean, extracellular H2O2 levels generally remain below the acutely cytotoxic threshold of ~1 x 10-5 M, perhaps partly through activity of co-occuring helper microorganisms that retain capacity to produce catalase [65,70].

Despite its radical nature and ability to react with biomolecules, NO functions widely as a signaling molecule [76,77]. Optimal in vivo concentrations of NO for phytoplankton growth vary significantly across taxa, from 10-8 to 10-9 mol extracellular NO L-1 [78]. NO is produced both biogenically through arginine dependent Nitric Oxide Synthases (NOS) or Nitric Oxide Associated Proteins (NOA) [79], as well as through abiotic processes including nitrite photolysis [80]. Overexpression of NO producing genes in the diatom Phaeodactylum tricornutum results in reduced growth, photosynthetic activity and ability to adhere to surfaces, and thus likely a decrease in biofilm formation [81]. [81] further suggest that NO influences cell motility in that diatoms increase NO production under stressful conditions thereby decreasing cellular adhesion, freeing the cell to find a more suitable habitat to which to adhere. NO can be enzymatically scavenged through Nitric Oxide Dioxygenase (NOD) or Nitric Oxide Reductases (NOR) [82] (Table 3), and may also react non-enzymatically with reduced glutathione (GSH) to form S-nitrosoglutathione (GSNO) [21,83]. Most cellular damage mediated by NO is attributed to the reaction of NO with O2•− to produce Peroxinitrite (ONOO). The reaction of NO with O2•− to produce ONOO- is limited by the extracellular concentration of NO and is not a major NO sink in seawater [16].

Other important ROS, Singlet Oxygen (1O2), Peroxynitrite (ONOO-) and Hydroxyl Radical (HO) are not known to be directly produced nor scavenged by enzymatic processes [[84]; [59,85–88]. In seawater, HO is present in concentrations of 10-18 to 10-15 mol L-1, and has a diffusion distance of only ~4.5 nm before destruction with a lifetime of µs [7,9]. Because of the high reactivity of HO, it is unlikely that there are any scavengers dedicated to HO specifically [59], although reactions with dissolved organic matter non-specifically scavenge extracellular HO [89].

The Black Queen Hypothesis

The Black Queen Hypothesis states that loss of function mutations may proceed so long as some interacting community members retain the function, and the function can occur outside a given cell [90]. The Black Queen Hypothesis was formulated on the basis of Prochlorococcus and H2O2. Prochlorococcus (the beneficiary) lost the gene katG encoding an enzyme which scavenges H2O2. Instead Prochlorococcus allows H2O2 outwards across the cell membrane to be dealt with by community members retaining the capacity to scavenge H2O2, thus saving Prochlorococcus the cost of maintaining the genes and metabolism for scavenging H2O2 [71,90].

Hypotheses and Significance

Given that ROS show differential abilities to cross cell membranes, and have widely different diffusion distances before destruction, we sought to study whether cell radius, colony formation, flagella or diatom cell shape influence genomic allocations to ROS production and scavenging across diverse marine phytoplankters.

Hypothesis 1 Cell radius across phytoplankton taxa does not influence the fraction of total gene content encoding O2•− production, nor scavenging. O2•− is highly toxic, and not readily able to cross biological membranes [17], so diffusional losses of O2•− from cells are limited and will be little influenced by cell size.

Hypothesis 2 Large phytoplankton allocate a smaller fraction of their total gene content to H2O2 and NO production and a larger fraction of their total gene content to H2O2 and NO scavenging.

H2O2 and NO have relatively low reactivity, with long intracellular and extracellular lifetimes leading to long potential diffusion paths before destruction. Both H2O2 and NO are uncharged and readily cross cell membranes (Table 1). Large cells have longer intracellular diffusional paths and a lower surface to volume ratios than do smaller cells [1]. Large cells are thus less prone to diffusional losses of intracellular H2O2 and NO. To maintain H2O2 and NO homeostasis in the face of slower diffusional losses of H2O2 or NO out of the cells to the environment, large phytoplankton may have a smaller fraction of their gene contents for H2O2 and NO production. In contrast, loss of function mutations on enzymes that scavenge H2O2 and NO would be more deleterious in large cells than in smaller cells [71,90].

Hypothesis 3 Pennate Diatoms allocate a larger fraction of their total gene content to H2O2 and NO production, and a smaller fraction of their total gene content to H2O2 and NO scavenging than do Centric Diatoms.

Pennate Diatoms have a small minimum radii even at large biovolumes due to their elongated shape [91]. This cell shape of pennate diatoms allows for more diffusion of H2O2 and NO across the cell membrane due to the short mean diffusion paths to the cell surface and high surface area to volume ratio. Noting that H2O2 and NO are permeable across biological membranes (Table 1). Pennate diatoms may have a larger fraction of their total gene content for H2O2 and NO production compared to centric diatoms. In contrast, pennate diatoms may have a smaller fraction of their gene content for H2O2 and NO scavenging, compared to centric diatoms.

Hypothesis 4 Colony forming phytoplankton have a smaller fraction of their total gene content encoding H2O2 and NO production, and a larger fraction of their total gene content encoding H2O2 and NO scavenging. Cell spacing in colony forming phytoplankton is so small that the diffusional spheres of H2O2 or NO diffusing outwards from cells overlap with nearby cells [75], thereby shifting the requirements to maintain homeostasis within cells of a colony.

Hypothesis 5 Flagellated phytoplankton have a larger fraction of their total gene content encoding H2O2 and NO production, and a smaller fraction of their total gene content encoding H2O2 and NO scavenging. Increased motility in flagellated cells allows movement away from cytotoxic levels of H2O2 and NO, possibly complementing scavenging.

Our work analyzed the fraction of the total genes in a genome or transcriptome associated with the metabolism of a particular ROS. The presence or absence of genes encoding specific ROS metabolizing enzymes may be caused by genetic drift, or may relate to a selective advantage linked to other metabolites of the same enzyme, rather than an enzymatic role in ROS metabolism, per se. Furthermore, the presence of a gene in a genome does not necessarily mean the encoded enzyme will be active, and closely related enzymes may mediate different activities in different organisms. The influence of non-enzymatic pathways such as carotenoids or tocopherols [42,92,93] may affect the hypotheses listed above, but were beyond the frame of this study.

Methods

Data Dictionary

Table 2 contains a data dictionary of variable names used in our analysis, their definitions and locations in code and data objects.

Bioinformatic Pipeline

We downloaded Genomes and/or Transcriptomes of 146 diverse marine phytoplankton (Table 4) from the National Center for Biotechnology Information (NCBI) [94]; Joint Genome Institute (JGI) [95,96]; iMicrobe [97], European Nucleotide Archive (ENA) [98]; pico-PLAZA [99], 1000 Plants (1KP) [100]; and the Reef Genomics Database [101] (Fig 1).

We implemented an automated pipeline using Snakemake [102] to pass gene sequences from downloaded genomes or transciptomes, in .fasta format, to eggNOG-Mapper 2.0.6 [103,104] and then used the DIAMOND algorithm [105] and the eggNOG 5.0 database [106], to annotate potential orthologs in each analyzed genome or transcriptome, using the following parameters: seed_ortholog_evalue = 0.001, seed_ortholog_score = 60, tax_scope = “auto,” go_evidence = “non-electronic,” query_cover = 20 and subject_cover = 0. The annotation generated for each gene model included (when available): the name of the matching ortholog (coded by eggNOG as ‘seed_eggNOG_ortholog’); E-value (coded by eggNOG as ‘seed_ortholog_evalue’); Score (coded by eggNOG as ‘seed_ortholog_score’); EC number (coded by eggNOG as ‘EC’); Kegg Orthology (KO) number (coded by eggNOG as ‘KEGG_ko’); Kegg Pathway (coded by eggNOG as ‘KEGG_Pathway’); Kegg Module (coded by eggNOG as ‘KEGG_Module’); Kegg Reaction (coded by eggNOG as ‘KEGG_Reaction’); Kegg Reaction Class (coded by eggNOG as ‘KEGG_rclass’); the predicted protein family (coded by eggNOG as ‘PFAMs’); Gene Ontology (GO) annotation (coded by eggNOG as ‘Gos’); as well as a description from eggNOG of the source organism of the matching ortholog (coded by eggNOG as ‘best_og_desc’). Note that comparison of sequences to the eggNOG 5.0 database generates non-supervised orthology annotations, and is subject to error if the underlying eggNOG annotation was inaccurate, or for functionally divergent orthologous gene sequences. The output of automatically annotated orthologs, from each genome or transcriptome, from the bioinformatic pipeline was compiled into one file CombinedHits.csv (to be submitted to the DRYAD database to support alternate analyses) (Fig 1).

**Summary Flowchart of Methods.**

Figure 1: Summary Flowchart of Methods.

Overview of Analysis of Annotated Genes

CombinedHits.csv was imported into a data frame (coded as ‘CombinedHits’) for analysis using R [107] running under RStudio [108], using the ‘tidyverse’ [109], ‘broom’ [110], ‘magrittr’ [111], ‘dplyr’ [112], ‘rcompanion’ [113], ‘gmodels’ [114], ‘stats’ [107], ‘AER’ [115] and ‘smatr’ [116] packages. Graphics and tables were generated using the ‘ggplot2’ [117], ‘cowplot’ [118], ‘glue’ [119], ‘kableExtra’ [120], ‘corrplot’ [121], ‘ggfortify’ [122,123], and ‘ggforce’ [124] packages (Fig 1). Formatted outputs were generated from RMarkdown files using the ‘knitr’ [125–127] and ‘bookdown’ [128] packages.

In parallel we assembled metadata from the literature and culture collection databases for each phytoplankter for which we obtained a genome or transcriptome, including the cell radii in µm from 100% of organisms; colony formation for 84% of organisms; cell shape from diatoms from 100% of diatoms; presence or absence of flagella as an index of potential motility from 100% of organisms, the genome size from all genomes and the total number of predicted gene models from 80% of organisms (Table 4); all stored in CellGenomeMetrics.csv (to be submitted to the DRYAD database for alternate analyses) (Fig 1). For organisms for which only transcriptomes were available, we only included datasets for which the total number of detected different transcripts was available, as a proxy for the total number of predicted genes. Strains of brackish origin were included but we did not include obligate freshwater strains in our analyses.

Citations were managed using the Zotero (www.zotero.org) open access reference manager connected to RStudio using the ‘citr’ [129] package. The Zotero library of citations for this paper is available at (https://www.zotero.org/groups/2333131/ros_phytoplankton).

We compared the Enzyme Commission Number (EC number) from CombinedHits to the BRENDA enzyme database [130] to identify enzymes annotated by BRENDA as ‘natural product’ or ‘natural substrate’ for H2O2, O2•− or NO in vivo (Table 3; Fig 1). We then used the EC Number to filter ‘CombinedHits’ to generate a subset containing only those orthologs encoding enzymes directly mediating metabolism, Production or Scavenging, of H2O2 , O2•− and NO.

From the ‘CombinedHits’ data frame, we filtered out some enzymes where the BRENDA annotations of ‘natural product’ or ‘natural substrate’ was questionable, in particular:

  • Superoxide oxidase (EC:1.10.3.17) carries a BRENDA annotation of ‘natural product’ for O2•−, despite the BRENDA citation stating that O2•− production from superoxide oxidase was only documented in vitro with an excess of ubiquinone [131].
  • D-amino-acid oxidase was removed from counts of genes encoding H2O2 production, as the enzyme does not produce H2O2 in vivo [132].
  • Bacterial non heme ferritin is listed under H2O2 production and scavenging as it produces H2O2 in the first of a two-step reaction and scavenges H2O2 in the second step [133].

From the subset of ‘CombinedHits’ of enzymes annotated for ROS metabolism, we grouped orthologs together by EC number and their Kegg Orthology number (KO number) and determined the occurrences of individual orthologs encoding each EC number, or KO number when EC number was not available, in a given organism. We merged this data subset with CellGenomeMetrics.csv to generate a dataset of genes encoding ROS metabolizing enzymes, as defined by the EC or KO number, along with characteristics of the source organism, combined into ‘MergedData.’

H2O2, O2•− and NO differ in reactivity, stability, diffusion distance, effects on biomolecules and roles in cell signaling (Table 1). We therefore generated the total gene counts coding for the production or scavenging of each different ROS in a given organism, which were used to generate Poisson or Quasi-Poisson regressions (Fig 1).

Data Validation & Justification of Statistical Analyses

Data from both genomes and transcriptomes were used in this analysis to gain wider representation from more taxa (Fig 11). Data from the taxa with the largest radii were derived wholly from transcriptomes. Aside from the prokaryote genomes, sourced solely from within the 45° north south latitude band, the sampled phytoplankton did not exhibit taxonomic biases in source latitude of isolation, but were primarily coastal (Fig 12). For 40 organisms we had both genomic and transcriptomic data, which we used to test assumptions on data distributions (Fig 15). As expected, data coverage from paired genomes and transcriptomes correlated well. Therefore, when both genomic and transcriptomic data were available from the same organism, we used genomic data in subsequent analyses (Table 4), but we used data from transcriptomes when genomes were not available. We validated the gene annotations generated by the snakemake bioinformatic pipeline by comparing the total number of genes encoding ROS metabolism data from a subset of ‘CombinedHits.csv’ to the total number of genes encoding ROS metabolism data from a manually annotated dataset generated during a pilot project (Fig 13) [134,135].

As expected, Fig 13 shows a significant correlation (Correlation of 0.87, p = 1.6×10-49) between manually generated ‘ROSGene_count’ and the automated ‘ROSGene_count’ from the snakemake pipeline.

**Comparison of log~10~ of the total number of genes in an organism ('log_GeneModels_count') to log~10~ of the median cell radius in µm ('log_Radius_um').** Colour corresponds to the taxonomic lineage ('Phylum'), whereas symbol shape corresponds to the source of the data, whether Genome or Transcriptome ('Ome'). Citations for data sources are in Supplementary Table S3.

Figure 2: Comparison of log10 of the total number of genes in an organism (‘log_GeneModels_count’) to log10 of the median cell radius in µm (‘log_Radius_um’). Colour corresponds to the taxonomic lineage (‘Phylum’), whereas symbol shape corresponds to the source of the data, whether Genome or Transcriptome (‘Ome’). Citations for data sources are in Supplementary Table S3.

**Comparison of log~10~ of the total number of ribosomal genes in an organism ('log10(RibosomeCount)') to log~10~ of the median cell radius in µm ('log_Radius_um').** Colour corresponds to the taxonomic lineage ('Phylum'), whereas symbol shape corresponds to the source of the data, whether Genome or Transcriptome ('Ome'). Citations for data sources are in Supplementary Table S3.

Figure 3: Comparison of log10 of the total number of ribosomal genes in an organism (‘log10(RibosomeCount)’) to log10 of the median cell radius in µm (‘log_Radius_um’). Colour corresponds to the taxonomic lineage (‘Phylum’), whereas symbol shape corresponds to the source of the data, whether Genome or Transcriptome (‘Ome’). Citations for data sources are in Supplementary Table S3.

XXX

MergedData %>% 
  filter(GeneModels_count != "0") %>%
  ggplot(aes(x = log10(RibosomeCount), y = log_GeneModels_count)) +
  ggConvexHull::geom_convexhull(alpha = 0.2, aes(fill = Phylum, color = Phylum)) +
  geom_point(aes(shape = Ome, colour = Phylum), size = 3) +
  scale_colour_manual(values =TaxaColors)+
  scale_fill_manual(values =TaxaColors)+
  scale_shape_manual(values = c(19,1)) +
  theme_bw() +
  coord_flip()+
  scale_x_continuous(limits = c(0,3))+
  scale_y_continuous(limits = c(3,6))+
  theme(panel.grid.major = element_blank(),
        panel.grid.minor = element_blank(),
        axis.text=element_text(size=12),
        axis.title=element_text(size=14),
        aspect.ratio = 1,
        legend.text=element_text(size=10),
        legend.title = element_text(size=12)) +
  labs(x = ~log[10] ~ "(Total Number of Ribosomal Genes)",
       y = ~log[10] ~ "(Total Number of Genes)")

Fig 14 shows that the frequencies of counts of genes encoding the metabolism of O2•−, H2O2 or NO within an organism are not normally distributed (Shapiro-Wilk Test [136] with a p-value of 6.4×10-30 for O2•− scavenging, 9.4×10-24 for H2O2 production, 5×10-25 for H2O2 scavenging, 1.2×10-18 for NO production and 1.5×10-30 for NO scavenging). The frequencies of gene counts instead follow a Poisson distribution. Therefore, for subsequent analyses we used Poisson or Quasi-Poisson regressions to compare the counts of genes that encode the production or scavenging of O2•−, H2O2 or NO within an organism to log10 of the median cell radius in µm. Code used to produce the Poisson and Quasi-Poisson models is on https://github.com/NaamanOmar/ROS_bioinfo/tree/master/ROSGenomicPatternsAcrossMarinePhytoplankton.

Quasi-Poisson regressions were used when the Poisson regression was over-dispersed (dispersion > 1, p < 0.05) as determined by the ‘AER’ package [115]. A Poisson regression followed by a chi-squared test, or a Quasi-Poisson regression followed by an F test, was used to obtain p-values [137], with an alpha value of ≤0.05 as the threshold for statistical significance of regressions.

The total number of genes in each organism increased with the median cell radius, and also varied among the taxonomic lineages (coded as ‘Phylum’) (Fig 2). Taxonomic lineage, in turn, interacts strongly with the median cell radius. For our analyses, we sought to detect effects of cell radius upon the fraction of total genes encoding ROS metabolism. We therefore included an offset of the total number of genes in the organism in the Poisson or Quasi-Poisson regressions, which is equivalent to normalizing the number of genes encoding the production or scavenging of H2O2, O2•− or NO, to the total number of genes in the organism (‘GeneModel_count’). We thereby offset the general increase in ‘GeneModel_count’ with increasing the median cell radius. Because of the strong interaction between the median cell radius and taxonomic lineage (Fig 11), we did not include Phylum as a co-variate in our subsequent regressions of normalized gene counts vs. median cell radius. Thus, we did not analyze specific influences of Phylum upon gene counts for ROS metabolism. Poisson or Quasi-Poisson regressions were run both with or without ‘Colony’ and ‘Flagella’ as co-variates.

To further investigate possible influences of colony formation, the presence of flagella or diatom cell shape (pennate or centric), upon the fraction of genes that encode the metabolism of H2O2, O2•− or NO, we used a Wilcoxon test [138].

Results and Discussion

Superoxide

Although there are enzymes producing O2•− [139], in the marine phytoplankton genomes and transcriptomes that we analysed, we did not detect any genes that encode for enzymes (Table 3) specifically producing O2•−, based on the BRENDA annotation of O2•− scavenging. It is however worth noting the presence of genes annotated as encoding NADPH Oxidase (NOX) in some phytoplankton genomes. NOX can produce either H2O2 or O2•− depending on the NOX isoform. NOX is included in our analyses as a H2O2 producer, in accordance with the BRENDA annotation of the enzyme (Table 3). Further analyses of the detected NOX isoforms might identify whether they are isoforms that produce O2•−. Sequences that are similar to Glutathione Reductase (GR) have been documented to produce enzymes that produce extracellular O2•− in the diatom Thalassiosira oceanica [139]. We found sequences annotated as GR across all phytoplankton genomes (Data not visualized), which likely include genes encoding enzymes producing O2•−. In any case, we do not expect phytoplankton to differentially allocate a changing fraction of their total gene content to O2•− production with increasing cell size, as O2•− is weakly membrane diffusible. Phytoplankton may need to maintain working extracellular concentrations of O2•−, since decreasing the extracellular concentration of O2•− can hinder cell growth [48]. [48] further explains that the downregulation of Superoxide Dismutase (SOD, EC:1.15.1.1) genes at peak light levels by Prochlorococcus [140] would allow Prochlorococcus to maintain ‘working levels’ of extracellular O2•−. Beyond putative enzymatically mediated production of O2•−, non-enzymatic processes associated with cells can also produce O2•−. Notably, O2•− is released to variable extents from side-reactions of electron transport [38,141,142] particularly under stress conditions.

Given that the O2•− is poorly diffusible across membranes, intracellularly produced O2•− has to be scavenged to limit detrimental reactions of O2•− [143]. As a result, cells universally maintain the genomic capacity to scavenge O2•−. All prokaryotic (Data not visualized) and eukaryotic (Fig 17) phytoplankton analyzed, with the exception of a single transcriptome from Micromonas polaris, have genes annotated as encoding the ubiquitous O2•− scavenging enzyme SOD. Genes annotated as encoding the enzyme Superoxide Oxidase (SOO, EC:1.10.3.17) were present in a few diatom species (Leptocylindrus danicus, Chaetoceros curvicetus and Thalassiosira minuscula CCMP1093) and prokaryotes (Crocosphaera spp.). Genes encoding the enzyme Superoxide Reductase (SOR, EC:1.15.1.2) were not detected in prokaryotes, but were detected in some diatoms (Pseudo-nitzschia fradulenta WWA7 and Seminavis robusta D6), and in the haptophyte Pleurochrysis carterae CCMP456.

The absence of transcripts encoding SOD from the Micromonas polaris transcriptome is likely due to low expression of SOD at the time that the mRNA was harvested for sequence analyses. Given that the O2•− is not membrane diffusible, intracellularly produced O2•− would have to be scavenged or would react with other cell components. As a result, organisms that do not have the ability to scavenge O2•− would have reduced fitness and so marine phytoplankton have not lost the genomic capacity to scavenge O2•−. To our knowledge, our results are the first detections of the occurences of genes encoding SOO and SOR in marine phytoplankton, and need confirmation by Multiple Sequence Alignment and enzyme assays, to determine whether our results stem from errors in annotation from the non-supervised grouping of orthologs (genes) by eggNOG, or indeed represent the sporadic presence of genes for SOO and SOR in the genomes of some phytoplankters. Further trends in genomic allocations to O2•− scavenging may emerge from the metallo-forms of SOD [144]. For example, in pilot runs with higher discrimination among metallo-forms we found that pico-prasinophytes encode Mn-SOD instead of the Fe-SOD encoded in genomes from larger green algal phytoplankters (Data not visualized).

Influence of Cell Size on Gene Counts for Enzymes Metabolizing O2•−

**Comparison of log~10~ (Total number of genes encoding O~2~^•−^ metabolizing enzymes ('SupOx_count') normalized to the total number of genes present in each organism ('GeneModels_count')) vs. the log~10~ (median cell radius in µm ('log_Radius_um')).** Poisson (solid line) or Quasi-Poisson (dashed line) regressions fitted to data ± Standard Error (dotted line). Regressions were run with (black line) or without (blue line) 'Colony' and 'Flagella' as co-variates. Selected prokaryote genomes are presented for comparison, but excluded from the presented regressions. Symbol color corresponds to taxon lineage ('Phylum').

Figure 4: Comparison of log10 (Total number of genes encoding O2•− metabolizing enzymes (‘SupOx_count’) normalized to the total number of genes present in each organism (‘GeneModels_count’)) vs. the log10 (median cell radius in µm (‘log_Radius_um’)). Poisson (solid line) or Quasi-Poisson (dashed line) regressions fitted to data ± Standard Error (dotted line). Regressions were run with (black line) or without (blue line) ‘Colony’ and ‘Flagella’ as co-variates. Selected prokaryote genomes are presented for comparison, but excluded from the presented regressions. Symbol color corresponds to taxon lineage (‘Phylum’).

With increasing cell radius, eukaryotic phytoplankton have a smaller fraction of their total genes encoding scavenging of O2•− (Fig 4, Blue line, Slope = -2.1×10-1 ± 7.1×10-2, p-value = 4.2×10-3, pseudo R2 = 0.0870217). The negative slope does not support our Hypothesis 1 that phytoplankton do not differentially allocate a changing fraction of their total gene content to O2•− production nor scavenging with increasing cell size. Including ‘Flagella’ and ‘Colony’ as co-variates in the regression results, however, in a slope that is not statistically different from zero (Fig 4, Black line, Slope = -6.7×10-2 ± 6.8×10-2, p-value = 3.3×10-1, pseudo R2 = 0.078205), driven by the influence of ‘Flagella’ (p-value = 3.7×10-2) but not ‘Colony’ (p-value = 8.6×10-1). Including data from selected prokaryotic phytoplankton did not qualitatively alter these results (Data not shown). O2•− metabolism in phytoplankton appears to be mediated by a nearly fixed set of core genes that do not change with increasing gene count, thus the fractional gene allocation to O2•− decreases as cell radius, and the co-varying total gene count increases. Gene dosage does not emerge as a factor in phytoplankton O2•− metabolism.

Influences of Flagella, Colony Formation and Cell Shape on Gene Counts for Enzymes Metabolizing O2•−

**Comparison of total number of genes encoding O~2~^•−^ scavenging enzymes ('SupOx_count') normalized to the total number of genes present in each organism ('GeneModels_count')) vs. the presence or absence of flagella in the organism.** Symbol color corresponds to taxon lineage ('Phylum'). Notch spans ± standard error of the median. Box spans median ± 1 quartile of the data. Whiskers span the range excluding outliers in the data. Citations for data sources can be found in Supplementary Table S3.

Figure 5: Comparison of total number of genes encoding O2•− scavenging enzymes (‘SupOx_count’) normalized to the total number of genes present in each organism (‘GeneModels_count’)) vs. the presence or absence of flagella in the organism. Symbol color corresponds to taxon lineage (‘Phylum’). Notch spans ± standard error of the median. Box spans median ± 1 quartile of the data. Whiskers span the range excluding outliers in the data. Citations for data sources can be found in Supplementary Table S3.

Consistent with the significant influence of flagella on the regressions vs. median cell radius (Fig 4), flagellated phytoplankton, irrespective of size, have a smaller proportion of their total gene content encoding O2•− scavenging (Fig 5, p-value = 4.3×10-3), than do non-flagellated phytoplankton. This suggests that cellular motility contributes to phytoplankton homeostasis of O2•−, possibly by supporting escape from localized pockets of O2•−.

SupOxColonyPlot <- knitr::include_graphics(file.path(Figures,"SupOxColonyPlot.png"))
# SupOxColonyPlot <- knitr::include_graphics(file.path(Figures,"SupOxColonyPlot.png"))

Setting aside any influence of cell size, colony and non-colony forming phytoplankton do not significantly differ in the fraction of their total gene content encoding O2•− scavenging (p-value = 8.1×10-1) (Data not visualized), consistent with limited membrane permeability for O2•− and thus limited colony level interactions in O2•− metabolism.

Pennate and centric diatoms have similar fractions of their genomes encoding O2•− scavenging (p-value = 9.7×10-1) (Data not visualized). Our results support our hypothesis that differential diffusional exchange across diatoms of different shape does not influence the fraction of total gene content that encodes O2•− scavenging enzymes, because O2•− diffusion is limited by the cell membrane irrespective of cell shape (Hypothesis 1). Differences between genomic patterns of pennate and centric diatoms may arise when comparing metallo-forms of SOD, noting that [145] found that pennate diatoms transcribe Cu/Zn-SOD but not Fe-SOD, whereas centric diatoms transcribe Fe-SOD more frequently than they transcribe Cu/Zn-SOD.

Hydrogen Peroxide

All prokaryotic (Fig 20) and eukaryotic (Fig 19) phytoplankton, with the exception of a single transcriptome from the prasinophyte Micromonas polaris, have genes encoding H2O2 producing enzymes, as they all carry gene(s) encoding the ubiquitous enzyme Superoxide Dismutase. Genes encoding three oxidases producing H2O2 are also widely distributed across phytoplankton genomes. Genes for copropophyrinogen oxidase are found across all eukaryotic and prokaryotic phytoplankton, with the exception of one transcriptome. Genes encoding thiol oxidase and acyl CoA oxidase are found in nearly all eukaryotic phytoplankton, with the exception of three transcriptomes. Genes encoding L-aspartate oxidase are found in nearly all prokaryotes, and all green algae, but are nearly absent from other eukaryotic taxa. Sarcosine oxidase is not present in small diatoms and small green algae, but is present in nearly all dinoflagellates and haptophytes. (S)-2-hydroxy-acid oxidase is found in most eukaryotic phytoplankton, but rarely in dinoflagellates.

All eukaryotic (Fig 19) and most prokaryotic phytoplankton (Fig 20), have genes encoding H2O2 scavenging enzymes. Some strains of Prochlorococcus and Synechococcus have lost all genomic capacity to scavenge H2O2, and appear to rely on co-occuring hosts for H2O2 scavenging [71,73,90].

The absence from catalase from most analyzed cyanobacterial genomes supports [146] who analyzed 44 different cyanobacterial genomes and found that only Nostoc punctiforme PCC73102 retained a full gene encoding catalase. In our analyses, only Synechococcus elongatus PCC11802 maintained a catalase encoding gene (Fig 20). In the greens, catalase has been lost from the smaller prasinophytes but is maintained in the larger greens (Fig 19). The loss of catalase from smaller green algae may be evidence of the Black Queen Hypothesis in action [90], in that H2O2 can passively diffuse out of the smaller green algae but diffuses less out of larger green algae. Loss of function mutations in catalase encoding genes in small algae are therefore less deleterious than they would be to large green algae. Catalase, with a KM of ~220 mM, may be poorly retained because the cells maintain some genomic capacity to scavenge H2O2 using the enzymes ascorbate peroxidase, glutathione peroxidase and Cytochrome C peroxidase (Fig 19), with KM in the low µM range [146].

Our results support an earlier suggestion that increased genomic capacity for H2O2 scavenging in Synechococcus compared to Prochlorococcus is a result of the larger size in Synechococcus compared to Prochlorococcus [71] (Fig 20). It is however important to note the vast differences between prokaryotic and eukaryotic phytoplankton, with most eukaryotic phytoplankton, irrespective of lineage, maintaining the genomic capacity to produce ascorbate peroxidase, glutathione peroxidase and Cytochrome C peroxidase (Fig 19). Peroxidases are often involved in pathways beyond simple ROS scavenging, including the Halliwell-Asada cycle for ascorbate peroxidase [147]. Ostreococcus, the smallest prasinophyte has a radius of 0.5 µm, comparable to that of the prokaryote Synechococcus (Table 4), and would therefore share a similar short diffusion path length. Nevertheless Ostreococcus, in common with other eukaryotes, retains genomic capacities to produce ascorbate peroxidase, glutathione peroxidase and Cytochrome C peroxidase, which may thus reflect the cost of being eukaryotic (Fig 19).

Influence of Cell Size on Gene Counts for Enzymes Metabolizing H2O2

**Comparison of log~10~ (Total number of genes encoding H~2~O~2~ metabolizing enzymes ('HyPe_count') normalized to the total number of genes present in each organism ('GeneModels_count')) vs. the log~10~ (median cell radius in µm ('log_Radius_um')).** Poisson (solid line) or Quasi-Poisson (dashed line) regressions fitted to data ± Standard Error (dotted line). Regressions were run with (black line) or without (blue line) 'Colony' and 'Flagella' as co-variates. Selected prokaryote genomes are presented for comparison, but excluded from the presented regressions. Symbol color corresponds to taxon lineage ('Phylum'). Citations for data sources are in Supplementary Table S3.

Figure 6: Comparison of log10 (Total number of genes encoding H2O2 metabolizing enzymes (‘HyPe_count’) normalized to the total number of genes present in each organism (‘GeneModels_count’)) vs. the log10 (median cell radius in µm (‘log_Radius_um’)). Poisson (solid line) or Quasi-Poisson (dashed line) regressions fitted to data ± Standard Error (dotted line). Regressions were run with (black line) or without (blue line) ‘Colony’ and ‘Flagella’ as co-variates. Selected prokaryote genomes are presented for comparison, but excluded from the presented regressions. Symbol color corresponds to taxon lineage (‘Phylum’). Citations for data sources are in Supplementary Table S3.

With increasing cell radius, eukaryotic phytoplankton have a smaller fraction of their total genes that encode the production of H2O2 (Fig 6, Blue line, Slope = -3.4×10-1 ± 5×10-2, p-value = 9.6×10-10, pseudo R2 = 0.3374895). Including ‘Flagella’ and ‘Colony’ as co-variates did not influence the negative slope of the fraction of total genes encoding H2O2 production with increasing median cell radius (Black line, ‘Flagella’ p-value = 8.4×10-1, ‘Colony’ p-value = 4.7×10-1). Including data from selected prokaryotic phytoplankton also did not qualitatively alter these results (Data not shown). The pattern of a smaller fraction of total genes for H2O2 production with increasing cell radius supports our Hypothesis 2 that larger phytoplankton counter decreasing diffusional loss of H2O2 out of cells by having lower genomic capacity for H2O2 production, while losses of genes encoding H2O2 producing enzymes are more costly to small phytoplankton (Fig 6). [7] found that a major influence upon the capacity for production of H2O2 is whether or not the organism can form blooms, with bloom forming species producing more H2O2. The ability to form blooms was not analyzed in our data as we did not find systematic information on potentials for bloom formation across taxa.

With increasing cell radius, eukaryotic phytoplankton also have a smaller fraction of their total genes that encode the capacity to scavenge H2O2 (Fig 6, Blue line, Slope = -3.2×10-1 ± 5.6×10-2, p-value = 1.4×10-7, pseudo R2 = 0.2628809). Including ‘Flagella’ and ‘Colony’ as co-variates did not influence the negative slope of the fraction of total genes encoding H2O2 scavenging with increasing median cell radius (Fig 6, Black line, ‘Flagella’ p-value = 4.1×10-1, ‘Colony’ p-value = 1.6×10-1). Including data from selected prokaryotic phytoplankton also did not qualitatively alter these results (Data not shown). A parallel analysis focusing only on small phytoplankton such as pico-cyanobacteria and pico-prasinophytes might yield different results as more genomes are sequenced, especially considering that [148] found that H2O2 added to seawater at a concentration of 1.6 mg L-1 did not affect cells with a radius larger than 1 to 1.5 μm, but differentially harmed the picoprasinophyte Micromonas pusilla.

Noting that the fraction of total genes allocated to both H2O2 production and scavenging decrease with increasing median cell radius, we found that the fraction of total genes that encode production of H2O2 decreases proportionally more with increasing cell radius than does the fraction of total genes that encode scavenging of H2O2 (4.6×10-50). The difference in slopes supports our Hypothesis 2 that large phytoplankton counter decreasing diffusional loss of H2O2 out of cells by having lower genomic capacity for H2O2 production relative to H2O2 scavenging. Because median cell radius co-varied with Taxa, we generally excluded Taxa as a co-variate from our regressions, in order to focus on any cross-taxon patterns driven by changing median cell radius. Nevertheless, representatives of the Ochrophyte Phylum alone spanned more than an order of magnitude in median cell radius. We therefore tested whether the log10(total number of genes encoding the metabolism of O2•−, H2O2 or NO) varied with the log10 (median cell radius) across the Ochrophytes alone (Fig 16). We found that across Ochrophytes, the fraction of total genes encoding the production of H2O2 decreased with increasing cell radius (Slope = -1.6×10-1 ± 9.4×10-2), although the p-value for the regression was only 1×10-1. This marginal decrease in the total number of genes encoding H2O2 production with increasing median cell radius in Ochrophytes again tends to support our Hypothesis 2, with data from within a single phylum to limit confounding influences of diverse evolutionary histories and cell biologies upon patterns.

Influences of Flagella, Colony Formation and Cell Shape on Gene Counts for Enzymes Metabolizing H2O2

**Comparison of total number of genes encoding H~2~O~2~ metabolizing enzymes ('HyPe_count') normalized to the total number of genes present in each organism ('GeneModels_count') vs. the presence or absence of flagella in the organism.** Symbol color corresponds to taxon lineage ('Phylum'). Notch spans ± standard error of the median. Box spans median ± 1 quartile of the data. Whiskers span the range excluding outliers in the data. Citations for data sources can be found in Supplementary Table S3.

Figure 7: Comparison of total number of genes encoding H2O2 metabolizing enzymes (‘HyPe_count’) normalized to the total number of genes present in each organism (‘GeneModels_count’) vs. the presence or absence of flagella in the organism. Symbol color corresponds to taxon lineage (‘Phylum’). Notch spans ± standard error of the median. Box spans median ± 1 quartile of the data. Whiskers span the range excluding outliers in the data. Citations for data sources can be found in Supplementary Table S3.

Setting aside influences of median cell radius, non-flagellated vs. flagellated phytoplankton exhibit no statistically significant difference in the fraction of their total genes encoding the production of H2O2 (p-value = 5.9×10-2), whereas non-flagellated phytoplankton have a significantly larger proportion of their total gene content encoding H2O2 scavenging (p-value = 6.9×10-3), than do flagellated phytoplankton (Fig 7). Thus, presence of flagella may contribute to the maintenance of H2O2 homeostasis across eukaryotic phytoplankton, but this potential influence is obscured by the range of cell sizes across eukaryotic phytoplankton when flagella are included as a co-variate in size regressions.

**Comparison of total number of genes encoding H~2~O~2~ metabolizing enzymes ('HyPe_count') normalized to the total number of genes present in each organism ('GeneModels_count') vs. the ability of the organism to form colonies.** Symbol color corresponds to taxon lineage ('Phylum'). Notch spans ± standard error of the median. Box spans median ± 1 quartile of the data. Whiskers span the range excluding outliers in the data. Citations for data sources can be found in Supplementary Table S3.

Figure 8: Comparison of total number of genes encoding H2O2 metabolizing enzymes (‘HyPe_count’) normalized to the total number of genes present in each organism (‘GeneModels_count’) vs. the ability of the organism to form colonies. Symbol color corresponds to taxon lineage (‘Phylum’). Notch spans ± standard error of the median. Box spans median ± 1 quartile of the data. Whiskers span the range excluding outliers in the data. Citations for data sources can be found in Supplementary Table S3.

Setting aside cell size influences, non-colony forming phytoplankton have a smaller proportion of their total gene content encoding both H2O2 production (p-value = 3.2×10-2), and also H2O2 scavenging (Fig 8, p-value = 1.7×10-2), than do colony forming phytoplankton. Looking at proportional change, we found that the decrease in the fraction of total genes encoding H2O2 production between non-colony and colony forming phytoplankton is smaller (-24.71%), than the decrease in the fraction of total genes encoding H2O2 scavenging (-27.79%). Colony forming phytoplankton may have more active H2O2 metabolism with a particular emphasis on H2O2 scavenging, consistent with stronger H2O2 exchange among closely spaced cells within a colony (Hypothesis 4) [75].

Pennate and centric diatoms do not show statistically significant differences in the fraction of their total gene content encoding the production (p-value = 1.9×10-1) nor the scavenging of H2O2 (p-value = 9.6×10-2). This result does not support our Hypothesis 3 that pennates have more genes encoding H2O2 producing enzymes due to their higher surface area to volume ratio (Data not visualized).

Nitric Oxide

In the genomes and transcriptomes that we analysed, Nitric Oxide Synthase (NOS, EC:1.14.13.39) was the most frequently occurring NO producing enzyme encoded (Fig 21), but was not encoded, or at least not annotated, among prokaryotic phytoplankton (Data not visualized).

Nitric Oxide Dioxygenase (NOD, EC:1.14.12.17) was the most frequently occurring of the NO scavenging enzymes (Fig 21). NOD sequences were identified in some eukaryotes, but were either not annotated, or not present in Prochlorococcus, most green algae and most centric diatoms. A NOS-like sequence has recently been identified in Synechococcus, that also has Nitric Oxide Dioxygenase-like function [149], and which might mediate NOD activity in some strains lacking annotated NOD sequences.

Influence of Cell Size on Gene Counts for Enzymes Metabolizing NO

**Comparison of log~10~ (Total number of genes encoding ^•^NO metabolizing enzymes ('NitOx_count') normalized to the total number of genes present in each organism ('GeneModels_count')) vs. the log~10~ (median cell radius in µm ('log_Radius_um')).** Poisson (solid line) or Quasi-Poisson (dashed line) regressions fitted to data ± Standard Error (dotted line). Regressions were run with (black line) or without (blue line) 'Colony' and 'Flagella' as co-variates. Selected prokaryote genomes are presented for comparison, but excluded from the presented regressions. Symbol color corresponds to taxon lineage ('Phylum').

Figure 9: Comparison of log10 (Total number of genes encoding NO metabolizing enzymes (‘NitOx_count’) normalized to the total number of genes present in each organism (‘GeneModels_count’)) vs. the log10 (median cell radius in µm (‘log_Radius_um’)). Poisson (solid line) or Quasi-Poisson (dashed line) regressions fitted to data ± Standard Error (dotted line). Regressions were run with (black line) or without (blue line) ‘Colony’ and ‘Flagella’ as co-variates. Selected prokaryote genomes are presented for comparison, but excluded from the presented regressions. Symbol color corresponds to taxon lineage (‘Phylum’).

With increasing cell radius eukaryotic phytoplankton do not vary in the fraction of total genes encoding the capacity to produce NO (Fig 9, Blue line, Slope = -2.5×10-1 ± 1.7×10-1, p-value = 1.5×10-1, pseudo R2 = 0.0239463). Including prokaryotes in the regression in Fig 9 does not substantially alter the interpretation (Data not visualized). We re-ran the quasipoisson, excluding those organisms that completely lack genes encoding enzymes for NO production (NitOx_count = 0, points along the x-axis), which resulted in a decreasing slope with increasing cell radius. Thus those phytoplankton with any detected capacity to produce NO indeed have a smaller fraction of their total genes encoding NO production with increasing radius (Fig 9, Blue line, Slope = -3.7×10-1 ± 1.1×10-1, p-value = 8.6×10-4, pseudo R2 = 0.133218). Including ‘Flagella’ and ‘Colony’ as co-factors to the regression that solely looks at organisms with the genomic capacity to produce NO resulted in a slope that is no longer significantly different from zero (Fig 9, Black line, Slope = -2.5×10-1 ± 1.4×10-1, p-value = 7×10-2, pseudo R2 = 0.0310994), driven by the influence of ‘Flagella’ (p-value = 1.4×10-4), but not ‘Colony’ (p-value = 1.8×10-1).

With increasing cell radius, eukaryotic phytoplankton do not vary in the fraction of their total genes encoding the capacity to scavenge NO, quasipoisson regression slope not significantly different from zero (Fig 9, Blue line, Slope = 1.3×10-1 ± 1.8×10-1, p-value = 4.7×10-1, pseudo R2 = 0.0372253). Including prokaryotes in the quasipoisson regression from Fig 9 does not alter the interpretation, as the slope is still not significantly different from zero. We re-ran the quasipoisson, excluding those organisms that completely lack genes encoding enzymes for NO scavenging (NitOx_count = 0, points along the x-axis), and found the exclusion did not alter the slope of the fraction of total genes encoding NO scavenging with increasing radius.

Non-enzymatic paths contribute to intracellular and extracellular NO production [150], and may explain the absences of genes encoding NO production from some genomes across taxonomic lineages. Alternately, NO homeostasis may be achieved in some lineages by regulating the active cellular uptake and release of intracellular NO, as has been recently demonstrated in humans [151]. Although NOD sequences have only been identified from phytoplankton through metatranscriptomic analyses, in diatoms, haptophytes and dinoflagellates [152], there is limited understanding as to what may contribute to the active removal of NO, and the lack of NO scavenging genes across multiple phytoplankters. More research is needed on possible contributions of NOD to the active removal of NO, as well as the NOS sequences detected in Synechococcus that also display NOD-like activity [149]. Perhaps the low toxicity of NO does not warrant the active removal of NO as long as the concentration does not exceed the toxic threshold. This explanation is plausible given that Platymonas helgolandica, Platymonas subcordiformis, Skeletonema costatum, Gymnodinium sp., and Prorocentrum donghaiense showed optimum growth in the presence of NO concentrations between 10-9 and 10-6 mol L-1 [153], which are higher than the concentrations found in the ocean (Table 1).

Influences of Flagella, Colony Formation and Cell Shape on Gene Counts for Enzymes Metabolizing NO

NitOxFlagella <- MergedData %>% 
  filter(Taxa != "Prokaryote", 
         NitOx %in% c("Production", "Scavenging")) %>%
  select(FileName, Genus, species, Strain, Name, Ome, Taxa, Rad1_um, Rad2_um, Rad3_um, Flagella, GenomeSize_mbp, GeneModels_count, Latitude, Longitude, Marine, HyPe, PennateCentric, SA_um2, Volume_um3, SAVol_um, Radius_um, log_Radius_um, log_GenomeSize_mbp, log_GeneModels_count, abs_Latitude, log_Volume_um3, log_SA_um2, log_SAVol_um, ROSGene_count, ECNumber, EnzymeName, ColonySpecies, NitOx_count, NitOx) %>%
  unique() %>%
  mutate(NitOx_countL = NitOx_count %>% as.logical() %>% as.numeric(),
         FlagellaL = ifelse(Flagella == "yes", 1, 0)) %>%
  select(FileName, NitOx_count, NitOx_countL, Flagella, FlagellaL,NitOx) %>%
  # filter(GeneModels_count != 0) %>% #This is because of introducing exposure
  nest(data = -c(NitOx)) %>%
  mutate(fit_b_offset = map(data, possibly(~glm(.$NitOx_countL ~ .$FlagellaL, data = ., family = binomial(link = "logit")), otherwise = NULL)),
                    param_b_offset = map(fit_b_offset, tidy),
                    pred_b_offset = map(fit_b_offset, augment))

Setting aside the influence of cell size, non-flagellated and flagellated phytoplankton do not show statistically significant differences in the fractions of total gene content encoding NO production (p-value = 6.3×10-1) nor NO scavenging (p-value = 8.9×10-1) (Data not visualized), suggesting NO does not have a generalized interaction with flagella across eukaryotic phytoplankton groups.

Presence absence = non-flagellated phytoplankton have 0.4655172 probability of having a nitric oxide producing gene as opposed to 0.6222222 in flagellated phytoplankton (p-value = 4.3×10-4. Non flagellated phytoplankton also have a 0.4482759 probability of having a nitric oxide scavenging gene, as opposed to 0.5555556 in flagellated phytoplankton (p-value = 3.1×10-2.

XXX

NitOxColonyPlot <- knitr::include_graphics(file.path(Figures,"NitOxColonyPlot.png"))

Comparing non-colony to colony forming phytoplankton does not show a statistically significant difference in the fraction of total gene content encoding NO production (p-value = 7.7×10-1) nor NO scavenging (p-value = 2.7×10-1) (Data not visualized), suggesting NO metabolism does not have a generalized role in colony formation across eukaryotic phytoplankton groups.

Presence absence = non-colony forming phytoplankton have 0.5882353 probability of having a nitric oxide producing gene as opposed to 0.5 in colony forming phytoplankton (p-value = 8.5×10-2. Non colony forming phytoplankton also have a 0.372549 probability of having a nitric oxide scavenging gene, as opposed to 0.5 in colony forming phytoplankton (p-value = 2.5×10-2.

**Comparison of total number of genes encoding ^•^NO metabolizing enzymes ('NitOx_count') normalized to the total number of genes present in each diatom ('GeneModels_count') vs. the growth form of the diatom ('PennateCentric').** Symbol color corresponds to taxon lineage ('Phylum'). Notch spans ± standard error of the median. Box spans median ± 1 quartile of the data. Whiskers span the range excluding outliers in the data. Citations for data sources can be found in Supplementary Table S3.

Figure 10: Comparison of total number of genes encoding NO metabolizing enzymes (‘NitOx_count’) normalized to the total number of genes present in each diatom (‘GeneModels_count’) vs. the growth form of the diatom (‘PennateCentric’). Symbol color corresponds to taxon lineage (‘Phylum’). Notch spans ± standard error of the median. Box spans median ± 1 quartile of the data. Whiskers span the range excluding outliers in the data. Citations for data sources can be found in Supplementary Table S3.

Most centric diatoms carry genes annotated as encoding NO producing enzymes, whereas most pennate diatoms do not (p-value = 6.2×10-3). In contrast, most centric diatoms lack genes annotated as encoding NO scavenging enzymes, whereas most pennate diatoms carry those genes (p-value = 3.8×10-5) (Fig 10).

The larger fractional gene allocation to NO production, and smaller fraction of genes that encode NO scavenging enzymes, in centric diatoms (Fig 10) counters our hypothesis that diffusion from pennate diatoms would drive gene allocations in favor of NO production (Hypothesis 3). Given the strong contrast in annotated NO metabolism genes, it is likely that NO has regulatory or signaling roles that vary systematically between pennate and centric diatoms, outside any diffusional influences. For example, NO inhibits diatom adhesion to substrate [81,154]. Pennates are more likely to grow adhered in biofilms [155], which may explain the striking differences in total gene allocation to NO production and scavenging. Alternately, [156] identified putative NOS sequences in the transcriptomes of three Pennate Diatom species (Pseudo-nitzschia arenysensis, Pseudo-nitzschia delicatissima and Pseudo-nitzschia multistriata), so it is possible the apparent lack of NO producing sequences in pennates is due to errors in the unsupervised annotations from eggNOG.

Presence absence = centric diatoms have 0.5238095 probability of having a nitric oxide producing gene as opposed to a 0.125 probability in pennate diatoms (p-value = 1.7×10-7. Centric diatoms also have a 0.1904762 probability of having a nitric oxide scavenging gene, as opposed to a 0.875 probability in colony forming phytoplankton (p-value = 4.7×10-13.

Summary

The differential reactivities, diffusion distances, diffusibilities across cell membranes, and roles in cell signaling of H2O2, O2•− and NO (Table 1) influence genomic allocation patterns for the production and scavenging of these three distinct ROS.

O2•− has high reactivity, short intracellular and extracellular lifetimes and limited cell membrane crossing. We did not find genes specifically encoding O2•− production in eukaryotic phytoplankton genomes. As expected, genes encoding O2•− scavenging were ubiquitous, but the fractional gene allocation to O2•− scavenging decreases as cell radius, and the co-varying total gene count increases, consistent with a nearly fixed set of core genes scavenging O2•− that do not change with increasing gene count.

H2O2 has lower reactivity, longer intracellular and extracellular lifetimes and readily crosses cell membranes. Across eukaryotic phytoplankton, the fraction of the total genes encoding H2O2 producing enzymes decreases with increasing cell radius, consistent with maintenance of ROS homeostasis in the face of slower diffusional losses from larger cells. The fraction of the total genes encoding H2O2 scavenging enzymes also decreases with increasing cell radius, but with a slope smaller than that for H2O2 producing enzymes. Presence of flagella and colony formation appear to influence H2O2 metabolism, supporting interactions between growth form and H2O2 homeostasis.

NO has low reactivity, long intracellular and extracellular lifetimes and readily crosses cell membranes. Neither the fraction of the total genes for NO production nor for scavenging changed significantly with increasing cell radius, consistent with relatively low cytotoxicity and roles of NO in taxonomically diverse regulatory systems. Pennate diatoms frequently lack genes annotated as encoding NO producing enzymes, whereas centric diatoms frequently lack genes annotated as encoding NO scavenging enzymes. This finding is not explicable by differential diffusional losses of NO, but may reflect distinct roles of NO in the regulatory systems of diatom lineages.

Funding Information

NMO was supported by the Mount Allison University Rice Memorial Graduate Fellowship and a New Brunswick Innovation Foundation STEM Graduate Award. KF was supported by NSERC Indigenous Undergraduate Summer Research Award and the MITACS GlobalLink internship. BB was supported by the Canada Research Chair in Phytoplankton Ecophysiology fund. DAC was supported by the Canada Research Chair in Phytoplankton Ecophysiology and by the Microbiology Institute of the Czech Academy of Science through project CZ.02.2.69/0.0/0.0/16_027/0007990 of the European Union Researcher Mobility program. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Open Data Statement

Code, genes annotated for ROS metabolism (MergedData.csv), MetaData.csv and DataDictionary.csv are stored on https://github.com/NaamanOmar/ROS_bioinfo/tree/master/ROSGenomicPatternsAcrossMarinePhytoplankton.

Annotations of all genes from genomes or transcriptomes of organisms used in this study, MetaData.csv and DataDictionary.csv are stored on DRYAD (DOI pending) to facilitate reuse for other purposes.

Supplementary Materials

Supplementary Figures

**Violin plot presenting the range of log~10~ of the median cell radius in µm ('log_Radius_um') for each taxonomic lineage ('Phylum').** Point colour corresponds to the source of the data, whether Genome or Transcriptome ('Ome'). Violin width indicates the fraction of all datapoints occuring at a cell radius ('log_Radius_um') within a phylum. Citations for data sources are in Supplementary Table S3.

Figure 11: Violin plot presenting the range of log10 of the median cell radius in µm (‘log_Radius_um’) for each taxonomic lineage (‘Phylum’). Point colour corresponds to the source of the data, whether Genome or Transcriptome (‘Ome’). Violin width indicates the fraction of all datapoints occuring at a cell radius (‘log_Radius_um’) within a phylum. Citations for data sources are in Supplementary Table S3.

Longitude and Latitude of isolation of analyzed organisms, overlaid on a world map Point colour corresponds to the taxonomic lineage ('Phylum'). Ocean colour corresponds to depth Citations for data sources are in Supplementary Table S3. Data used to generate world map produced from the 'ggOceanMaps' R package [@vihtakariGgOceanMapsPlotData2021]

Figure 12: Longitude and Latitude of isolation of analyzed organisms, overlaid on a world map Point colour corresponds to the taxonomic lineage (‘Phylum’). Ocean colour corresponds to depth Citations for data sources are in Supplementary Table S3. Data used to generate world map produced from the ‘ggOceanMaps’ R package [157]

**Comparison of paired counts of particular genes encoding ROS Production or Scavenging from manual and automatic annotations taken from the same organism.** Data was drawn from a subset of genomes and transcriptomes which were both manually and automatically annotated. Colour corresponds to the 'Gene' Points are jittered to avoid overlapping, resulting in blocks around frequently occuring counts. Dashed line is placed at 1:1 where Manual and Automated counts would be equal. Citations for data sources in Supplemental Data Table S4.

Figure 13: Comparison of paired counts of particular genes encoding ROS Production or Scavenging from manual and automatic annotations taken from the same organism. Data was drawn from a subset of genomes and transcriptomes which were both manually and automatically annotated. Colour corresponds to the ‘Gene’ Points are jittered to avoid overlapping, resulting in blocks around frequently occuring counts. Dashed line is placed at 1:1 where Manual and Automated counts would be equal. Citations for data sources in Supplemental Data Table S4.

**Histogram of occurrences of number of total genes, in a genome or transcriptome, (y axis) that code for the production of enzymes that produce or scavenge H~2~O~2~, O~2~^•−^ or ^•^NO *in vivo*.** Symbol color corresponds to taxon lineage ('Taxa')

Figure 14: Histogram of occurrences of number of total genes, in a genome or transcriptome, (y axis) that code for the production of enzymes that produce or scavenge H2O2, O2•− or NO in vivo. Symbol color corresponds to taxon lineage (‘Taxa’)

**Comparison of paired counts of particular genes encoding ROS Production or Scavenging from the genome ('ROSGene_count.g') or transcriptome ('ROSGene_count.t') taken from the same organism.** Data was drawn from a subset of analyzed organisms for which both genome and transcriptome were available. Colour corresponds to the taxonomic lineage ('Phylum') Points are jittered to avoid overlapping, resulting in blocks around frequently occuring counts. Dashed line is at 1:1 where 'ROSGene_count.g' and 'ROSGene_count.t' would be equal. Citations for data sources are in Supplementary Table S3.

Figure 15: Comparison of paired counts of particular genes encoding ROS Production or Scavenging from the genome (‘ROSGene_count.g’) or transcriptome (‘ROSGene_count.t’) taken from the same organism. Data was drawn from a subset of analyzed organisms for which both genome and transcriptome were available. Colour corresponds to the taxonomic lineage (‘Phylum’) Points are jittered to avoid overlapping, resulting in blocks around frequently occuring counts. Dashed line is at 1:1 where ‘ROSGene_count.g’ and ‘ROSGene_count.t’ would be equal. Citations for data sources are in Supplementary Table S3.

**Comparison of log~10~ (Total number of genes encoding H~2~O~2~, O~2~^•−^ or ^•^NO metabolizing enzymes normalized to the total number of genes present in each Ochrophyte) vs. the log~10~( median cell radius in µm).** Poisson (solid line) or Quasi-Poisson (dashed line) regressions fitted to data ± Standard Error (dotted line). Regressions were run without (blue line) 'Colony' and 'Flagella' as co-variates. Citations for data sources are in Supplementary Table S3.

Figure 16: Comparison of log10 (Total number of genes encoding H2O2, O2•− or NO metabolizing enzymes normalized to the total number of genes present in each Ochrophyte) vs. the log10( median cell radius in µm). Poisson (solid line) or Quasi-Poisson (dashed line) regressions fitted to data ± Standard Error (dotted line). Regressions were run without (blue line) ‘Colony’ and ‘Flagella’ as co-variates. Citations for data sources are in Supplementary Table S3.

**Summary of O~2~^•−^ scavenging enzymes encoded within genomes and transcriptomes of eukaryotic phytoplankton analyzedTaxa are ordered from top to bottom along the left according to increasing median cell diameter within each taxonomic lineage.** Symbol colour corresponds to taxonomic lineages (‘Taxa’).Filled data points indicate that the data obtained from that organism was sourced from a genome, and unfilled data points were sourced from a transcriptome. The size of the symbol increases with the number of members of each enzyme found within each genome or transcriptome. Symbol absence means no sequences known to encode the enzyme family of interest were found in the target genome or transcriptome.

Figure 17: Summary of O2•− scavenging enzymes encoded within genomes and transcriptomes of eukaryotic phytoplankton analyzedTaxa are ordered from top to bottom along the left according to increasing median cell diameter within each taxonomic lineage. Symbol colour corresponds to taxonomic lineages (‘Taxa’).Filled data points indicate that the data obtained from that organism was sourced from a genome, and unfilled data points were sourced from a transcriptome. The size of the symbol increases with the number of members of each enzyme found within each genome or transcriptome. Symbol absence means no sequences known to encode the enzyme family of interest were found in the target genome or transcriptome.

SupOxFlagRiboPlot <- knitr::include_graphics(file.path(Figures,"SupOxFlagRiboPlot.png"))
**Comparison of total number of genes encoding O~2~^•−^ scavenging enzymes ('SupOx_count') normalized to the total number of genes present in each organism ('GeneModels_count')) vs. the presence or absence of flagella in the organism.** Symbol color corresponds to taxon lineage ('Phylum'). Notch spans ± standard error of the median. Box spans median ± 1 quartile of the data. Whiskers span the range excluding outliers in the data. Citations for data sources can be found in Supplementary Table S3.

Figure 18: Comparison of total number of genes encoding O2•− scavenging enzymes (‘SupOx_count’) normalized to the total number of genes present in each organism (‘GeneModels_count’)) vs. the presence or absence of flagella in the organism. Symbol color corresponds to taxon lineage (‘Phylum’). Notch spans ± standard error of the median. Box spans median ± 1 quartile of the data. Whiskers span the range excluding outliers in the data. Citations for data sources can be found in Supplementary Table S3.

**Summary of H~2~O~2~ metabolizing enzymes encoded within genomes and transcriptomes of eukaryotic phytoplankton analyzed, faceted by whether the enzymes Produce or Scavenge H~2~O~2~.** Taxa are ordered from top to bottom along the left according to increasing median cell diameter within each taxonomic lineage. Symbol colour corresponds to taxonomic lineages (‘Taxa’). Filled data points indicate that the data obtained from that organism was sourced from a genome, and unfilled data points were sourced from a transcriptome. The size of the symbol increases with the number of members of each enzyme found within each genome or transcriptome. Symbol absence means no sequences known to encode the enzyme family of interest were found in the target genome or transcriptome.

Figure 19: Summary of H2O2 metabolizing enzymes encoded within genomes and transcriptomes of eukaryotic phytoplankton analyzed, faceted by whether the enzymes Produce or Scavenge H2O2. Taxa are ordered from top to bottom along the left according to increasing median cell diameter within each taxonomic lineage. Symbol colour corresponds to taxonomic lineages (‘Taxa’). Filled data points indicate that the data obtained from that organism was sourced from a genome, and unfilled data points were sourced from a transcriptome. The size of the symbol increases with the number of members of each enzyme found within each genome or transcriptome. Symbol absence means no sequences known to encode the enzyme family of interest were found in the target genome or transcriptome.

**Summary of H~2~O~2~ metabolizing enzymes encoded within genomes of prokaryotic phytoplankton analyzed, faceted by whether the enzymes Produce or Scavenge H~2~O~2~.** Taxa are ordered from top to bottom along the left according to increasing median cell diameter within each taxonomic lineage. Symbol colour corresponds to the genus of the prokaryote.Filled data points indicate that the data obtained from that organism was sourced from a genome. The size of the symbol increases with the number of members of each enzyme found within each genome or transcriptome. Symbol absence means no sequences known to encode the enzyme family of interest were found in the target genome or transcriptome.

Figure 20: Summary of H2O2 metabolizing enzymes encoded within genomes of prokaryotic phytoplankton analyzed, faceted by whether the enzymes Produce or Scavenge H2O2. Taxa are ordered from top to bottom along the left according to increasing median cell diameter within each taxonomic lineage. Symbol colour corresponds to the genus of the prokaryote.Filled data points indicate that the data obtained from that organism was sourced from a genome. The size of the symbol increases with the number of members of each enzyme found within each genome or transcriptome. Symbol absence means no sequences known to encode the enzyme family of interest were found in the target genome or transcriptome.

HyPeColonyRiboPlot <- knitr::include_graphics(file.path(Figures,"HyPeColonyRiboPlot.png"))
**Summary of ^•^NO metabolizing enzymes encoded within genomes and transcriptomes of eukaryotic phytoplankton analyzed, faceted by whether the enzymes Produce or Scavenge ^•^NO.** Taxa are ordered from top to bottom along the left according to increasing median cell diameter within each taxonomic lineage. Symbol colour corresponds to taxonomic lineages (‘Taxa’). Filled data points indicate that the data obtained from that organism was sourced from a genome, and unfilled data points were sourced from a transcriptome. The size of the symbol increases with the number of members of each enzyme found within each genome or transcriptome. Symbol absence means no sequences known to encode the enzyme family of interest were found in the target genome or transcriptome.

Figure 21: Summary of NO metabolizing enzymes encoded within genomes and transcriptomes of eukaryotic phytoplankton analyzed, faceted by whether the enzymes Produce or Scavenge NO. Taxa are ordered from top to bottom along the left according to increasing median cell diameter within each taxonomic lineage. Symbol colour corresponds to taxonomic lineages (‘Taxa’). Filled data points indicate that the data obtained from that organism was sourced from a genome, and unfilled data points were sourced from a transcriptome. The size of the symbol increases with the number of members of each enzyme found within each genome or transcriptome. Symbol absence means no sequences known to encode the enzyme family of interest were found in the target genome or transcriptome.

Supplementary Tables

Table 2: Variable names, definitions, units, and first location of occurence in code, used for our data.
Variable Units Definition
FileName Name of source genome or transcriptome file composed of three components - Name, Nucleic acid and Genome or Transcriptome, separated by dashes
EnzymeName Name of ROS producing or scavenging enzyme
ECNumber Enzyme Commission Number of enzyme
HyPe Indicates whether the enzyme Produces or Scavenges Hydrogen Peroxide
SupOx Indicates whether the enzyme Produces or Scavenges Superoxide
NitOx Indicates whether the enzyme Produces or Scavenges Nitric Oxide
Genus Genus of the organism
species Species of the organism
Strain Strain of the organism
Ome Genome or Transcriptome
Taxa Taxonomic group that the organism falls in, typically class, phyla or superphyla
Rad1_um µm Smallest median radius (in µm) along all three axes
Rad2_um µm Second largest median radius (in µm) along all three axes
Rad3_um µm Largest median radius (in µm) along all three axes
Flagella Refers to whether or not the organism has flagella
GenomeSize_mbp mbp Size of the genome in megabasepairs
GeneModels_count count Total number of genes in genome
Latitude ° Source latitude from which the organism was sampled in degrees
Longitude ° Source longitude from which the organism was sampled in degrees
Marine States whether the organism is Marine or not
PennateCentric Solely for Diatoms, indicates whether the Diatom is a Pennate or a Centric Diatom
ColonySpecies Indicates whether an organism can form colonies or not
KO Kegg Orthology Number
HyPe_PriSec If the Enzyme Produces or Scavenges Hydrogen Peroxide, HyPePriSec indicates whether Hydrogen Peroxide is a Primary or Secondary product or substrate
HyPe_PriSec_citekey Citation for HyPe
NitOx_PriSec If the Enzyme Produces or Scavenges Nitric Oxide, NitOxPriSec indicates whether Nitric Oxide is a Primary or Secondary product or substrate
NitOx_PriSec_citekey Citation for NitOx_PriSec
SupOx_PriSec If the Enzyme Produces or Scavenges Superoxide, SupOxPriSec indicates whether Superoxide is a Primary or Secondary product or substrate
SupOx_PriSec_citekey Citation for SupOx_PriSec
Reaction Reaction of the enzyme to produce or scavenge ROS; if multiple reactions are present, they are separated by “;”
Reaction_citation Citation for Reaction
ECTreeL1 First Level Description of EC Tree
ECTreeL2 Second Level Description of EC Tree
ECTreeL3 Third Level Description of EC Tree
ProdAndScav An enzyme that is annotated as both producing and scavenging the substrate
RemoveEnzyme Remove an enzyme as per methods
Notes Notes
query_name query sequence name
seed_eggNOG_ortholog best protein match in eggNOG
seed_ortholog_evalue best protein match (e-value)
seed_ortholog_score best protein match (bit-score)
eggNOG OGs a comma-separated, clade depth-sorted (broadest to narrowest), list of Orthologous Groups (OGs) identified for this query. Note that each OG is represented in the following format: _id|tax_name
narr_og_name _id|tax_name for the narrowest OG found for this query.
narr_og_cat COG category corresponding to narr_og_name
narr_og_desc Description corresponding to narr_og_name
best_og_name _id|tax_name for the OG chosen based on –tax_scope.
best_og_cat COG category corresponding to best_og_name
best_og_desc Description corresponding to best_og_name
Preferred_name EGGNOG annotation of enzyme name
GOs Gene Ontology
KEGG_ko Kegg Orthology Number
KEGG_Pathway KEGG Pathway
KEGG_Module KEGG Module
KEGG_Reaction KEGG Reaction
KEGG_rclass KEGG rclass
BRITE Hierarchical classification systems capturing functional hierarchies of various biological objects
KEGG_TC KEGG Transporter Classification
CAZy Carbohydrate-Active enZYmes
BiGG_Reaction Biochemical Genetic and Genomic
PFAMs Protein Families
ROSGene_count count Count of genes encoding an enzyme by EC number
TotalDetectedEnzymes count Total count of detected enzyme encoding genes in the genome
SA_um2 µm2 Surface area in µm2
Volume_um3 µm3 Volume in µm3
SAVol_um µm-1 Surface area to Volume ratio in µm-1
Radius_um µm Smallest median radius (in µm) along all three axes
log_Radius_um µm log10 of Radius_µm
log_GenomeSize_mbp mbp log10 of GenomeSize_mbp
log_GeneModels_count count log10 of GeneModel_count
log_Gene_count count log10 of Gene_count
abs_Latitude ° absolute of Latitude
log_Volume_um3 µm3 log10 of Volume_µm3
log_SA_um2 µm2 log10 of SA_µm2
log_SAVol_um µm-1 log10 of SAVol_perµm
Name Combination of Genus, species, Strain
Phylum Taxonomic group that the organism falls in, typically class, phyla or superphyla
HyPe_count Total count of genes encoding Production or Scavenging of HyPe
NitOx_count Total count of genes encoding Production or Scavenging of NitOx
SupOx_count Total count of genes encoding Production or Scavenging of SupOx
TotalROSGene_count Total count of genes encoding Production or Scavenging of HyPe, NitOx and SupOx
TransWGenomePresent A transcriptome that also has a genome published; these are used for quality control
Table 3: Enzyme Commission Number, Kegg Orthology Number, Enzyme Name and ROS Substrate Metabolised
ECNumber KO EnzymeName SupOx HyPe NitOx
1.1.3.4 Glucose oxidase Production
1.1.3.B4 Glycerol oxidase Production
1.1.3.5 K21840 Hexose oxidase Production
1.1.3.6 K03333 Cholesterol oxidase Production
1.1.3.7 Aryl-Alcohol oxidase Production
1.1.3.8 K00103 L-gulonolactone oxidase Production
1.1.3.9 K04618 Galactose oxidase Production
1.1.3.10 K23272 Pyranose oxidase Production
1.1.3.11 L-sorbose oxidase Production
1.1.3.12 K18607 Pyridoxine 4-oxidase Production
1.1.3.13 K17066 Alcohol oxidase Production
1.1.3.15 K00104;K11517 (S)-2-hydroxy-acid oxidase Production
1.1.3.16 K10724 Ecdysone oxidase Production
1.1.3.17 K17755 choline oxidase Production
1.1.3.18 secondary-alcohol oxidase Production
1.1.3.19 4-hydroxymandelate oxidase (decarboxylating) Production
1.1.3.20 K17756 long-chain-alcohol oxidase Production
1.1.3.21 K00105 glycerol-3-phosphate oxidase Production
1.1.3.27 hydroxyphytanate oxidase Production
1.1.3.30 polyvinyl-alcohol oxidase Production
1.1.3.37 K00107 D-arabinono-1,4-lactone oxidase Production
1.1.3.38 K20153 vanillyl-alcohol oxidase Production
1.1.3.39 nucleoside oxidase (H2O2-forming) Production
1.1.3.40 D-mannitol oxidase Production
1.1.3.41 K00594 alditol oxidase Production
1.1.3.42 K20550 prosolanapyrone-II oxidase Production
1.1.3.45 K15949 aclacinomycin-N oxidase Production
1.1.3.46 K16422 4-hydroxymandelate oxidase Production
1.1.3.47 K16873 5-(hydroxymethyl)furfural oxidase Production
1.1.3.48 K19714 3-deoxy-alpha-D-manno-octulosonate 8-oxidase Production
1.1.5.13 K15736 (S)-2-hydroxyglutarate dehydrogenase Production
1.1.98.3 K16653 decaprenylphospho-beta-D-ribofuranose 2-dehydrogenase Production
1.1.99.B3 glucooligosaccharide oxidase Production
1.1.99.18 cellobiose dehydrogenase (acceptor) Production
1.2.3.1 K00157 aldehyde oxidase Production
1.2.3.3 K00158 pyruvate oxidase Production
1.2.3.4 oxalate oxidase Production
1.2.3.5 glyoxylate oxidase Production
1.2.3.7 K11817 indole-3-acetaldehyde oxidase Production
1.2.3.9 aryl-aldehyde oxidase Production
1.2.3.14 K09842 abscisic-aldehyde oxidase Production
1.2.3.15 K20929 (methyl)glyoxal oxidase Production
1.3.3.3 K00228 coproporphyrinogen oxidase Production
1.3.3.4 K00231 protoporphyrinogen oxidase Production
1.3.3.6 K00232 acyl-CoA oxidase Production
1.3.3.8 K22089 tetrahydroberberine oxidase Production
1.3.3.11 K06137 pyrroloquinoline-quinone synthase Production
1.3.3.12 L-galactonolactone oxidase Production
1.3.3.13 K21375 albonoursin synthase Production
1.3.3.14 K15949 aclacinomycin-A oxidase Production
1.3.3.16 K05897 oxazoline dehydrogenase Production
1.3.98.1 K00226 dihydroorotate dehydrogenase (fumarate) Production
1.4.3.1 K00272 D-aspartate oxidase Production
1.4.3.B1 K09471 gamma-glutamylputrescine oxidase Production
1.4.3.2 K03334 L-amino-acid oxidase Production
1.4.3.3 K00273 D-amino-acid oxidase Production
1.4.3.4 K00274 monoamine oxidase Production
1.4.3.5 K00275 pyridoxal 5’-phosphate synthase Production
1.4.3.7 D-glutamate oxidase Production
1.4.3.8 ethanolamine oxidase Production
1.4.3.10 K03343 putrescine oxidase Production
1.4.3.11 L-glutamate oxidase Production
1.4.3.12 cyclohexylamine oxidase Production
1.4.3.13 K00277 protein-lysine 6-oxidase Production
1.4.3.14 L-lysine oxidase Production
1.4.3.15 D-glutamate(D-aspartate) oxidase Production
1.4.3.16 K00278 L-aspartate oxidase Production
1.4.3.19 K03153 glycine oxidase Production
1.4.3.20 K17831 L-lysine 6-oxidase Production
1.4.3.21 K00276 primary-amine oxidase Production
1.4.3.22 K11182 diamine oxidase Production
1.4.3.23 K19884 7-chloro-L-tryptophan oxidase Production
1.4.3.25 K21639 L-arginine oxidase Production
1.5.3.1 K00301;K00302;K00303;K00304;K00305 sarcosine oxidase Production
1.5.3.4 N6-methyl-lysine oxidase Production
1.5.3.5 K19826 (S)-6-hydroxynicotine oxidase Production
1.5.3.6 K19890 (R)-6-hydroxynicotine oxidase Production
1.5.3.7 K00306 L-pipecolate oxidase Production
1.5.3.10 K00309 dimethylglycine oxidase Production
1.5.3.12 dihydrobenzophenanthridine oxidase Production
1.5.3.13 K00308 N1-acetylpolyamine oxidase Production
1.5.3.14 K13366 polyamine oxidase (propane-1,3-diamine-forming) Production
1.5.3.15 N8-acetylspermidine oxidase (propane-1 Production
1.5.3.16 K12259 spermine oxidase Production
1.5.3.17 K13367;K17839 non-specific polyamine oxidase Production
1.5.3.18 K17833 L-saccharopine oxidase Production
1.5.8.4 K00315 dimethylglycine dehydrogenase Production
1.6.3.1 K13411 NAD(P)H oxidase (H2O2-forming) Production
1.6.3.3 K17870 NADH oxidase (H2O2-forming) Production
1.6.3.5 K18208 renalase Production
1.7.3.1 K19823 nitroalkane oxidase Production
1.7.3.3 K00365 factor-independent urate hydroxylase Production
1.8.3.1 K00387 sulfite oxidase Production
1.8.3.2 K10758;K17783;K17891 thiol oxidase Production
1.8.3.3 glutathione oxidase Production
1.8.3.4 K17285 methanethiol oxidase Production
1.8.3.5 K05906 prenylcysteine oxidase Production
1.8.3.6 K05906 farnesylcysteine lyase Production
1.10.3.4 K20219 o-aminophenol oxidase Production
1.10.3.5 3-hydroxyanthranilate oxidase Production
1.10.3.6 rifamycin-B oxidase Production
1.11.1.1 K05910 NADH peroxidase Production
1.13.12.9 K21795 phenylalanine 2-monooxygenase Production
1.13.12.16 K00459 nitronate monooxygenase Production
1.14.99.24 steroid 9alpha-monooxygenase Production
1.14.99.66 K11450 [histone H3]-N6,N6-dimethyl-L-lysine4 FAD-dependent demethylase Production
1.15.1.1 K00518;K04564;K04565;K16627 superoxide dismutase Scavenging Production
1.15.1.2 K05919 superoxide reductase Scavenging Production
1.16.3.2 K00522;K02217 bacterial non-heme ferritin Production
1.17.1.4 K00087;K11177;K11178;K13479;K13481;K13482 xanthine dehydrogenase Production
1.17.3.2 xanthine oxidase Production
1.21.3.3 K00307 reticuline oxidase Production
1.21.3.7 K20501 tetrahydrocannabinolic acid synthase Production
1.21.3.8 K20502 cannabidiolic acid synthase Production
2.1.1.148 K03465 thymidylate synthase (FAD) Production
4.1.1.107 K01618 3,4-dihydroxyphenylacetaldehyde synthase Production
4.1.1.108 K22327 4-hydroxyphenylacetaldehyde synthase Production
4.1.1.109 K22328 phenylacetaldehyde synthase Production
1.1.3.17 K17755 choline oxidase Scavenging
1.2.3.9 aryl-aldehyde oxidase Scavenging
1.3.98.5 K00435;K25033 hydrogen peroxide-dependent heme synthase Scavenging
1.4.3.3 K00273 D-amino-acid oxidase Scavenging
1.11.1.1 K05910 NADH peroxidase Scavenging
1.11.1.B2 chloride peroxidase (vanadium-containing) Scavenging
1.11.1.3 fatty-acid peroxidase Scavenging
1.11.1.5 K00428 cytochrome-c peroxidase Scavenging
1.11.1.6 K03781 catalase Scavenging
1.11.1.7 K00430;K10788;K12550;K19511;K21201 peroxidase Scavenging
1.11.1.8 K00431 iodide peroxidase Scavenging
1.11.1.9 K00432 glutathione peroxidase Scavenging
1.11.1.10 K00433;K17990 chloride peroxidase Scavenging
1.11.1.11 K00434 L-ascorbate peroxidase Scavenging
1.11.1.13 K20205 manganese peroxidase Scavenging
1.11.1.14 K23515 lignin peroxidase Scavenging
1.11.1.16 versatile peroxidase Scavenging
1.11.1.19 K15733 dye decolorizing peroxidase Scavenging
1.11.1.21 K03782 catalase-peroxidase Scavenging
1.11.1.23 K12903;K22392 (S)-2-hydroxypropylphosphonic acid epoxidase Scavenging
1.11.2.1 K21820 unspecific peroxygenase Scavenging
1.11.2.2 K10789 myeloperoxidase Scavenging Scavenging
1.11.2.3 K17991 plant seed peroxygenase Scavenging
1.11.2.4 K15629 fatty-acid peroxygenase Scavenging
1.11.2.5 K20208 3-methyl-L-tyrosine peroxygenase Scavenging
1.11.2.6 K24287 L-tyrosine peroxygenase Scavenging
1.14.18.1 K00505 tyrosinase Scavenging
1.16.3.2 K00522;K02217 bacterial non-heme ferritin Scavenging
1.21.98.2 K19885 dichlorochromopyrrolate synthase Scavenging
1.10.3.17 K12262 superoxide oxidase Scavenging
1.10.3.17 K12262 superoxide oxidase Production
1.7.2.1 K00368 nitrite reductase (NO-forming) Production
1.7.2.5 K04561 nitric oxide reductase (cytochrome c) Production
1.7.2.7 K20932;K20933;K20934 hydrazine synthase Production
1.7.6.1 K22962 nitrite dismutase Production
1.14.13.39 K13240;K13241;K13242;K13427 nitric-oxide synthase (NADPH) Production
1.7.2.1 K00368 nitrite reductase (NO-forming) Scavenging
1.7.2.5 K04561 nitric oxide reductase (cytochrome c) Scavenging
1.14.12.17 K05916 nitric oxide dioxygenase Scavenging
Table 4: Metadata for each organism
Genus species Strain FileName Marine Ome Taxa PennateCentric PennateCentric_citekey Rad1_um Rad2_um Rad3_um Rad_citekey Flagella Flagella_citekey GenomeSize_mbp GeneModels_count Genome_ref Genome_citekey Database AccessionInfo LiveLink Latitude Longitude LogLat_citekey ColonySpecies Colony_citekey Notes
Guillardia theta CCMP2712 Guillardia_theta_CCMP2712-aa-gen yes Genome Cryptophyte 2.500 2.500 4.750 [158] yes [159] 87.16000 24840 [160] JGI 16067 https://mycocosm.jgi.doe.gov/Guith1/Guith1.home.html 41.22640 -73.063900 [158] 0 [158]
Guillardia theta CCMP2712 Guillardia_theta_CCMP2712-dna-trans yes Transcriptome Cryptophyte 2.500 2.500 4.750 [158] yes [159] 1.00000 0 [160] JGI 16067 https://mycocosm.jgi.doe.gov/Guith1/Guith1.home.html 41.22640 -73.063900 [158] 0 [158]
Phaeodactylum tricornutum CCAP1055/1 Phaeodactylum_tricornutum_CCAP10551-aa-gen yes Genome Diatom Pennate [161] 1.750 1.750 21.000 [161] no [162] 27.45070 10402 [163] NCBI ASM15095v2 https://www.ncbi.nlm.nih.gov/assembly/GCF_000150955.2/ 54.00000 4.000000 [158] 0 [164]
Phaeodactylum tricornutum CCAP1055/1 Phaeodactylum_tricornutum_CCAP10551-dna-trans yes Transcriptome Diatom Pennate [161] 1.750 1.750 21.000 [161] no [162] 1.00000 0 [163] NCBI ASM15095v2 https://www.ncbi.nlm.nih.gov/assembly/GCF_000150955.2/ 54.00000 4.000000 [158] 0 [164]
Fistulifera solaris JPCC_DA0580 Fistulifera_solaris_JPCCDA0580-aa-gen yes Genome Diatom Pennate [165] 1.900 1.900 3.375 [165] no [166] 49.74000 20429 [167] JGI Fistulifera solaris JPCC DA0580: Fisso1_GeneCatalog_proteins_20200805.aa.fasta.gz https://genome.jgi.doe.gov/portal/pages/dynamicOrganismDownload.jsf?organism=Fisso0 28.15000 129.240000 [165] 0 [165]
Fistulifera solaris JPCC_DA0580 Fistulifera_solaris_JPCCDA0580-dna-trans yes Transcriptome Diatom Pennate [165] 1.900 1.900 3.375 [165] no [166] 1.00000 0 [167] JGI Fistulifera solaris JPCC DA0580: Fisso1_GeneCatalog_transcripts_20200805.nt.fasta.gz https://genome.jgi.doe.gov/portal/pages/dynamicOrganismDownload.jsf?organism=Fisso1 28.15000 129.240000 [165] 0 [165]
Thalassiosira pseudonana CCMP1335 Thalassiosira_pseudonana_CCMP1335-aa-gen yes Genome Diatom Centric [158] 2.250 2.250 2.500 [158] no [168] 32.10000 11776 [169] PicoPlaza proteome.tps.tfa.gz https://bioinformatics.psb.ugent.be/plaza/versions/pico-plaza/download 40.75600 -72.820000 [158] 0 [170]
Thalassiosira pseudonana CCMP1335 Thalassiosira_pseudonana_CCMP1335-dna-trans yes Transcriptome Diatom Centric [158] 2.250 2.250 2.500 [158] no [168] 1.00000 0 [169] NCBI ASM14940v2 https://www.ncbi.nlm.nih.gov/genome/?term=txid35128[orgn] 40.75600 -72.820000 [158] 0 [170]
Thalassiosira oceanica CCMP1005 Thalassiosira_oceanica_CCMP1005-aa-gen yes Genome Diatom Centric [158] 4.000 4.000 4.000 [158] no [166] 92.19000 34642 [171] NCBI PRJNA36595 https://www.ncbi.nlm.nih.gov/genome/3436 33.18330 -65.850000 [158] 0 [172]
Thalassiosira oceanica CCMP1005 Thalassiosira_oceanica_CCMP1005-dna-trans yes Transcriptome Diatom Centric [158] 4.000 4.000 4.000 [158] no [166] 1.00000 0 [171] NCBI PRJNA36595 https://www.ncbi.nlm.nih.gov/genome/3437 33.18330 -65.850000 [158] 0 [172]
Coscinodiscus wailesii CCMP2513 Coscinodiscus_wailesii_CCMP2513-aa-trans yes Transcriptome Diatom Centric [158] 44.000 44.000 44.000 [158] no [166] 1.00000 0 [173] MMETSP MMETSP1066 https://figshare.com/articles/dataset/Marine_Microbial_Eukaryotic_Transcriptome_Sequencing_Project_re-assemblies/3840153 31.43220 -80.358000 [158] 0 [174]
Chaetoceros debilis MM31A_1 Chaetoceros_debilis_MM31A_1-aa-trans yes Transcriptome Diatom Centric [175] 6.500 7.500 12.000 [175] no [166] 1.00000 19453 [176] iMicrobe Chaetoceros-debilis-MM31A_1.pep.fa.gz ftp://ftp.imicrobe.us/camera/combined_assemblies/ 0 [177]
Conticribra_Thalassiosira weissflogii CCMP1010 Thalassiosira_weissflogii_CCMP1010-aa-trans yes Transcriptome Diatom Centric [158] 5.500 5.500 7.750 [158] no [166] 1.00000 21329 [176] iMicrobe Thalassiosira-weissflogii-CCMP1010.pep.fa.gz ftp://ftp.imicrobe.us/camera/combined_assemblies/ 37.00000 -65.000000 [158] 0 [170]
Cyclotella cryptica CCMP332 Cyclotella_cryptica_CCMP332-aa-gen yes Genome Diatom Centric [178] 4.500 4.500 7.000 [178] no [166] 161.70000 21121 [178] UCLA Nuclear genome http://genomes.mcdb.ucla.edu/Cyclotella/download.html 41.35500 -70.655000 0 [170]
Nannochloropsis oceanica CCMP1779 Nannochloropsis_oceanica_CCMP1779-aa-gen yes Genome Eustigmatophyte 1.500 1.500 1.500 [158] no [179] 28.74000 10641 [180] JGI 1143084 https://mycocosm.jgi.doe.gov/Nanoce1779_2/Nanoce1779_2.home.html 29.00000 48.000000 [158] 0 [181]
Nannochloropsis oceanica CCMP1779 Nannochloropsis_oceanica_CCMP1779-dna-trans yes Transcriptome Eustigmatophyte 1.500 1.500 1.500 [158] no [179] 1.00000 0 [180] JGI 1143084 https://mycocosm.jgi.doe.gov/Nanoce1779_2/Nanoce1779_2.home.html 29.00000 48.000000 [158] 0 [181]
Nannochloropsis gaditana CCMP526 Nannochloropsis_gaditana_CCMP526-aa-gen yes Genome Eustigmatophyte 1.500 1.500 2.500 [158] no [182] 30.30000 9052 [181] NCBI ASM24072v1 https://www.ncbi.nlm.nih.gov/assembly/GCF_000240725.1/ 32.83330 -9.000000 [158] 0 [183]
Nannochloropsis gaditana CCMP526 Nannochloropsis_gaditana_CCMP526-dna-trans yes Transcriptome Eustigmatophyte 1.500 1.500 2.500 [158] no [182] 1.00000 0 [181] NCBI ASM24072v1 https://www.ncbi.nlm.nih.gov/assembly/GCF_000240725.1/ 32.83330 -9.000000 [158] 0 [183]
(Nannochloropsis) Microchloropsis salina CCMP1776 Nannochloropsis_salina_CCMP1776-aa-gen yes Genome Eustigmatophyte 1.250 1.250 1.500 [158] no [184] 27.75730 10522 [185] NCBI ASM456527v1 https://www.ncbi.nlm.nih.gov/genome/75342?genome_assembly_id=739695 55.75000 -4.960000 [158] 0 [186]
Ostreococcus tauri RCC1115 Ostreococcus_tauri_RCC1115-aa-gen yes Genome Green 0.500 0.500 0.500 [187] no [188] 14.76000 8218 [189] NCBI GCA_002158475.1 https://phycocosm.jgi.doe.gov/Ostta1115_2/Ostta1115_2.home.html 42.90000 3.030000 [187] 0 [190]
Ostreococcus tauri RCC1115 Ostreococcus_tauri_RCC1115-dna-trans yes Transcriptome Green 0.500 0.500 0.500 [187] no [188] 1.00000 0 [189] JGI Ostreococcus tauri RCC1115 v1.0: Ostta1115_2_GeneCatalog_transcripts_20170131.nt.fasta.gz https://phycocosm.jgi.doe.gov/Ostta1115_2/Ostta1115_2.home.html 42.90000 3.030000 [187] 0 [190]
Ostreococcus tauri RCC4221 Ostreococcus_tauri_RCC4221-aa-gen yes Genome Green 0.500 0.500 0.500 [187] no [188] 13.03000 7669 NCBI GCF_000214015.3 https://www.ncbi.nlm.nih.gov/assembly/GCF_000214015.3 42.40000 3.600000 [187] 0 [190]
Ostreococcus tauri RCC4221 Ostreococcus_tauri_RCC4221-dna-trans yes Transcriptome Green 0.500 0.500 0.500 [187] no [188] 1.00000 0 NCBI GCF_000214015.2 https://www.ncbi.nlm.nih.gov/assembly/GCF_000214015.3 42.40000 3.600000 [187] 0 [190]
Bathycoccus prasinos RCC1105 Bathycoccus_prasinos_RCC1105-aa-gen yes Genome Green 0.750 0.750 0.750 [191] no [191] 15.07000 7900 [191] NCBI ASM222023v1 https://www.ncbi.nlm.nih.gov/genome/12309?genome_assembly_id=323984 42.45000 3.530000 [187] 0 [192]
Bathycoccus prasinos RCC1105 Bathycoccus_prasinos_RCC1105-dna-trans yes Transcriptome Green 0.750 0.750 0.750 [191] no [191] 1.00000 0 [191] NCBI ASM222023v1 https://www.ncbi.nlm.nih.gov/genome/12309?genome_assembly_id=323984 42.45000 3.530000 [187] 0 [192]
Micromonas pusilla CCMP1545 Micromonas_pusilla_CCMP1545-aa-gen yes Genome Green 1.250 1.250 1.500 [158] yes [193] 21.95830 10242 [194] NCBI GCA_000151265.1 https://www.ncbi.nlm.nih.gov/genome/501?genome_assembly_id=281477 50.36500 -4.170000 [158] 0 [195]
Micromonas pusilla CCMP1545 Micromonas_pusilla_CCMP1545-dna-trans yes Transcriptome Green 1.250 1.250 1.500 [158] yes [193] 1.00000 0 [194] NCBI GCA_000151265.1 https://www.ncbi.nlm.nih.gov/genome/501?genome_assembly_id=281477 50.36500 -4.170000 [158] 0 [195]
Coccomyxa subellipsoidea C-169 Coccomyxa_sp_C169-aa-gen yes Genome Green 2.400 2.400 3.600 [196] no [196] 49.00000 9629 [196] JGI Coccomyxa_C169_v2_filtered_proteins.fasta.gz https://mycocosm.jgi.doe.gov/Coc_C169_1/Coc_C169_1.home.html 0 [197]
Coccomyxa subellipsoidea C-169 Coccomyxa_sp_C169-dna-trans yes Transcriptome Green 2.400 2.400 3.600 [196] no [196] 1.00000 0 [196] JGI Coccomyxa_C169_v2_filtered_transcripts.fasta.gz https://mycocosm.jgi.doe.gov/Coc_C169_1/Coc_C169_1.home.html 0 [197]
Dunaliella salina CCAP 19/18 Dunaliella_salina_CCAP1918-aa-gen yes Genome Green 4.500 4.500 8.000 [198] yes [199] 343.70000 18801 [200] JGI Dunaliella salina CCAP19/18: Project: 1014865, Dunsal1_1_GeneCatalog_proteins_20200511.aa.fasta.gz https://genome.jgi.doe.gov/portal/pages/dynamicOrganismDownload.jsf?organism=Dunsal1_1 0 [187]
Dunaliella salina CCAP 19/18 Dunaliella_salina_CCAP1918-dna-trans yes Transcriptome Green 4.500 4.500 8.000 [198] yes [199] 1.00000 0 [200] JGI Dunaliella salina CCAP19/18: Project: 1014865, Dunsal1_1_GeneCatalog_transcripts_20200511.nt.fasta.gz https://genome.jgi.doe.gov/portal/pages/dynamicOrganismDownload.jsf?organism=Dunsal1_1 0 [187]
Picocystis salinarum CCMP1897 Picocystis_salinarum_CCMP1897-aa-trans yes Transcriptome Green 1.750 1.750 1.750 [158] no [201] 1.00000 6947 [176] iMicrobe Picocystis-salinarum-CCMP1897.pep.fa.gz ftp://ftp.imicrobe.us/camera/combined_assemblies/ 37.78000 -122.350000 [158] 0 [202]
Emiliania huxleyi CCMP1516 Emiliania_huxleyi_CCMP1516-aa-gen yes Genome Haptophyte 3.250 3.250 3.250 [203] yes [203] 167.67600 38554 [204] NCBI GCA_000372725.1 https://www.ncbi.nlm.nih.gov/assembly/678008 -2.66670 -82.716700 [158] 0 [205]
Emiliania huxleyi CCMP1516 Emiliania_huxleyi_CCMP1516-dna-trans yes Transcriptome Haptophyte 3.250 3.250 3.250 [203] yes [203] 1.00000 0 [204] NCBI GCA_000372725.1 https://www.ncbi.nlm.nih.gov/assembly/678008 -2.66670 -82.716700 [158] 0 [205]
Tisochrysis lutea
Tisochrysis_lutea-aa-gen yes Transcriptome Haptophyte 2.500 2.500 3.000 [206] yes [206] 54.40000 20582 [207] SEANOE Protein_sequencesV1 https://www.seanoe.org/data/00361/47171/ 0 [206]
Aureococcus anophagefferens CCMP1984 Aureococcus_anophagefferens_CCMP1984-aa-gen yes Genome Pelagophyte 1.750 1.750 1.750 [208] no [209] 56.66060 11522 [210] NCBI GCA_000186865.1 v 1.0 https://www.ncbi.nlm.nih.gov/assembly/GCF_000186865.1/ 40.66670 -73.270000 [158] 0 [211]
Aureococcus anophagefferens CCMP1984 Aureococcus_anophagefferens_CCMP1984-dna-trans yes Transcriptome Pelagophyte 1.750 1.750 1.750 [208] no [209] 1.00000 0 [210] NCBI GCA_000186865.1 v 1.0 https://www.ncbi.nlm.nih.gov/assembly/GCF_000186865.1/ 40.66670 -73.270000 [158] 0 [211]
Pelagophyceae sp. CCMP2097 Pelagophyceae_sp_CCMP2097-aa-gen yes Genome Pelagophyte 4.500 4.500 4.500 [158] yes [212] 85.82000 19402 JGI Pelagophyceae sp. CCMP2097 v1.0: Project: 1020062, Pelago2097_1_GeneCatalog_proteins_20160408.aa.fasta.gz https://genome.jgi.doe.gov/portal/pages/dynamicOrganismDownload.jsf?organism=Pelago2097_1 77.00140 -77.238300 [158] 0 [187]
Pelagophyceae sp. CCMP2097 Pelagophyceae_sp_CCMP2097-dna-trans yes Transcriptome Pelagophyte 4.500 4.500 4.500 [158] yes [212] 1.00000 0 JGI Pelagophyceae sp. CCMP2097 v1.0: Project: 1020062, Pelago2097_1_GeneCatalog_transcripts_20160408.nt.fasta.gz https://genome.jgi.doe.gov/portal/pages/dynamicOrganismDownload.jsf?organism=Pelago2097_1 77.00140 -77.238300 [158] 0 [187]
Pelagococcus subviridis CCMP1429 Pelagococcus_subviridis_CCMP1429-aa-trans yes Transcriptome Pelagophyte 1.500 1.500 1.500 [158] yes [213] 1.00000 21365 [176] iMicrobe Pelagococcus-subviridis-CCMP1429.pep.fa.gz ftp://ftp.imicrobe.us/camera/combined_assemblies/ 49.91660 -145.116600 [158] 0 [187]
Porphyridium purpureum CCMP1328 Porphyridium_purpureum_CCMP1328-aa-gen yes Genome Red 3.250 3.250 3.250 [158] no [214] 19.70000 8355 [214] RUTGERS P. purpureum‚àö√§genomic assembly (nucleotide) http://cyanophora.rutgers.edu/porphyridium/ 41.52640 -70.670000 [158] 0 [215]
Minidiscus variabilis CCMP495 Minidiscus_variabilis_CCMP495-aa-gen yes Genome Diatom Centric [158] 1.750 1.750 6.000 [158] no [166] 76.19000 25179 JGI JGI Minidiscus variabilis CCMP495 v1.0: Project: 1094205, Mintr2_GeneCatalog_proteins_20160618.aa.fasta.gz https://genome.jgi.doe.gov/portal/pages/dynamicOrganismDownload.jsf?organism=Mintr2 43.00000 -68.000000 [158] 0 [174]
Minidiscus variabilis CCMP495 Minidiscus_variabilis_CCMP495-dna-trans yes Transcriptome Diatom Centric [158] 1.750 1.750 6.000 [158] no [166] 1.00000 0 JGI JGI Minidiscus variabilis CCMP495 v1.0: Project: 1094205, Mintr2_GeneCatalog_transcripts_20160618.nt.fasta.gz https://genome.jgi.doe.gov/portal/pages/dynamicOrganismDownload.jsf?organism=Mintr2 43.00000 -68.000000 [158] 0 [174]
Prochlorococcus marinus AS9601 Prochlorococcus_marinus_AS9601-aa-gen yes Genome Prokaryote 0.350 0.350 0.550 [74] no [216] 1.67000 1899 [217] NCBI GCF_000015645.1 https://www.ncbi.nlm.nih.gov/nuccore/NC_008816.1 19.12000 67.100000 [74] 0 [218]
Prochlorococcus marinus marinus CCMP1375 Prochlorococcus_marinus_marinus_CCMP1375-aa-gen yes Genome Prokaryote 0.350 0.350 0.550 [74] no [216] 1.75000 1897 [219] NCBI GCF_000007925.1 https://www.ncbi.nlm.nih.gov/assembly/GCF_000007925.1 30.00000 -60.000000 [158] 0 [218]
Prochlorococcus marinus MIT9313 Prochlorococcus_marinus_MIT9313-aa-gen yes Genome Prokaryote 0.350 0.350 0.550 [74] no [216] 2.41000 2457 [220] NCBI GCF_000011485.1 https://www.ncbi.nlm.nih.gov/assembly/GCF_000011485.1 37.30000 -68.140000 [74] 0 [218]
Prochlorococcus marinus NATL2A Prochlorococcus_marinus_NATL2A-aa-gen yes Genome Prokaryote 0.350 0.350 0.550 [74] no [216] 1.84000 2010 [217] NCBI GCF_000012465.1 https://www.ncbi.nlm.nih.gov/nuccore/NC_007335.2 38.59000 40.330000 [74] 0 [218]
Prochlorococcus marinus pastoris CCMP1986 Prochlorococcus_marinus_pastoris_CCMP1986-aa-gen yes Genome Prokaryote 0.350 0.350 0.700 [158] no [216] 1.66000 1908 [220] NCBI GCF_000011465.1 https://www.ncbi.nlm.nih.gov/nuccore/NC_005072.1 35.00000 20.000000 [158] 0 [218]
Alexandrium fundyense CCMP1719 Alexandrium_fundyense_CCMP1719-aa-trans yes Transcriptome Dinoflagellate 17.500 17.500 17.500 [158] yes [213] 1.00000 11897 [176] iMicrobe Alexandrium-fundyense-CCMP1719.pep.fa https://datacommons.cyverse.org/browse/iplant/home/shared/imicrobe/camera/camera_mmetsp_ncgr/combined_assemblies/Alexandrium-fundyense-CCMP1719 43.10000 -70.782000 [158] 0 [221]
Amphidinium carterae CCMP1314 Amphidinium_carterae_CCMP1314-aa-trans yes Transcriptome Dinoflagellate 5.500 5.500 8.000 [158] yes [213] 1.00000 42459 [176] iMicrobe Amphidinium-carterae-CCMP1314/ ftp://ftp.imicrobe.us/camera/combined_assemblies/Amphidinium-carterae-CCMP1314/ 41.56000 -70.583000 [158] 0 [222]
Aureoumbra lagunensis CCMP1510 Aureoumbra_lagunensis_CCMP1510-aa-trans yes Transcriptome Pelagophyte 2.750 2.750 2.750 [158] yes [213] 1.00000 16737 [176] iMicrobe Aureoumbra-lagunensis-CCMP1510/ ftp://ftp.imicrobe.us/camera/combined_assemblies/Aureoumbra-lagunensis-CCMP1510/ 27.47080 -97.320000 [158] 0 [223]
Chaetoceros affinis CCMP159 Chaetoceros_affinis_CCMP159-aa-trans yes Transcriptome Diatom Centric [158] 3.750 3.750 11.000 [158] no [166] 1.00000 19072 [176] iMicrobe Chaetoceros-affinis-CCMP159/ ftp://ftp.imicrobe.us/camera/combined_assemblies/Chaetoceros-affinis-CCMP159/ 29.42000 -86.105000 [158] 0 [187]
Chattonella subsalsa CCMP2191 Chattonella_subsalsa_CCMP2191-aa-trans yes Transcriptome Raphidophyte 10.750 10.750 21.000 [158] yes 1.00000 25803 [176] iMicrobe Chattonella-subsalsa-CCMP2191/ ftp://ftp.imicrobe.us/camera/combined_assemblies/Chattonella-subsalsa-CCMP2191/ 38.59000 -75.100000 [158] 0 [187]
Heterosigma akashiwo CCMP2393 Heterosigma_akashiwo_CCMP2393-aa-trans yes Transcriptome Raphidophyte 6.500 6.500 9.000 [158] yes [224] 1.00000 40801 [176] iMicrobe Heterosigma-akashiwo-CCMP2393/ ftp://ftp.imicrobe.us/camera/combined_assemblies/Heterosigma-akashiwo-CCMP2393/ 38.69910 -75.109700 [158] 0 [225]
Heterosigma akashiwo CCMP452 Heterosigma_akashiwo_CCMP452-aa-trans yes Transcriptome Raphidophyte 6.000 6.000 6.250 [158] yes [224] 1.00000 27465 [176] iMicrobe Heterosigma-akashiwo-CCMP452/ ftp://ftp.imicrobe.us/camera/combined_assemblies/Heterosigma-akashiwo-CCMP452/ 41.00000 -73.000000 [158] 0 [225]
Isochrysis galbana CCMP1323 Isochrysis_galbana_CCMP1323-aa-trans yes Transcriptome Haptophyte 1.500 1.500 2.500 [158] yes [226] 1.00000 45931 [176] iMicrobe Isochrysis-galbana-CCMP1323/ ftp://ftp.imicrobe.us/camera/combined_assemblies/Isochrysis-galbana-CCMP1323/ 54.08500 -4.770000 [158] 0 [158]
Isochrysis sp. CCMP1244 Isochrysis_sp_CCMP1244-aa-trans yes Transcriptome Haptophyte 2.000 2.000 2.000 [158] yes 1.00000 38449 [176] iMicrobe Isochrysis-sp-CCMP1244/ ftp://ftp.imicrobe.us/camera/combined_assemblies/Isochrysis-sp-CCMP1244/ 38.30480 -69.619200 [158] 0 [158]
Lingulodinium polyedra CCMP1738 Lingulodinium_polyedra_CCMP1738-aa-trans yes Transcriptome Dinoflagellate 17.500 17.500 18.750 [158] yes [213] 1.00000 104588 [176] iMicrobe Lingulodinium-polyedra-CCMP1738/ ftp://ftp.imicrobe.us/camera/combined_assemblies/Lingulodinium-polyedra-CCMP1738/ 27.80000 -97.130000 [158] 0 [187]
Rhodomonas sp. CCMP768 Rhodomonas_sp_CCMP768-aa-trans yes Transcriptome Cryptophyte 4.750 4.750 8.750 [158] yes 1.00000 31952 [176] iMicrobe Rhodomonas-sp-CCMP768/ ftp://ftp.imicrobe.us/camera/combined_assemblies/Rhodomonas-sp-CCMP768/ -39.00000 176.000000 [158] 0 [187]
Scrippsiella trochoidea CCMP3099 Scrippsiella_trochoidea_CCMP3099-aa-trans yes Transcriptome Dinoflagellate 12.500 12.500 16.000 [158] yes [213] 1.00000 113860 [176] iMicrobe Scrippsiella-trochoidea-CCMP3099/ ftp://ftp.imicrobe.us/camera/combined_assemblies/Scrippsiella-trochoidea-CCMP3099/ 27.31220 -82.601000 [158] 0 [227]
Cyanobium sp. NIES-981 Cyanobium_sp_NIES-981-aa-gen yes Genome Prokaryote 0.380 0.380 0.650 [228] no [228] 3.02000 2883 [229] NCBI GCA_900088535.1 ASM90008853v1 scaffolds: 1 contigs: 1 N50: 3,021,545 L50: 1 https://www.ncbi.nlm.nih.gov/genome/13678?genome_assembly_id=319205 0 [230]
Lotharella globosa CCCM811 Lotharella_globosa_CCCM811-aa-trans yes Transcriptome Green 4.600 4.600 4.600 [231] yes [231] 1.00000 29293 [176] iMicrobe Lotharella-globosa-CCCM811.pep.fa ftp://ftp.imicrobe.us/camera/combined_assemblies/ 13.83330 144.750000 [158] 0 [232] CCCM811 is also known as CCMP2314 or CCMP1729
Prochlorococcus sp. MIT0601 Prochlorococcus_sp._MIT0601-aa-gen yes Genome Prokaryote 0.350 0.350 0.550 [74] no [216] 1.71000 1854 [233] NCBI GCA_000760175.1 https://www.ncbi.nlm.nih.gov/genome/13712?genome_assembly_id=210094 22.75000 -158.000000 [233] 0 [218]
Prochlorococcus marinus GP2 Prochlorococcus_marinus_GP2-aa-gen yes Genome Prokaryote 0.350 0.350 0.550 [74] no [216] 1.62000 1845 [233] NCBI GCA_000759885.1 ASM75988v1 scaffolds: 11 contigs: 11 N50: 416,038 L50: 2 https://www.ncbi.nlm.nih.gov/genome/164?genome_assembly_id=209980 8.00000 136.000000 [234] 0 [218]
Prochlorococcus sp. MIT0604 Prochlorococcus_sp._MIT0604-aa-gen yes Genome Prokaryote 0.350 0.350 0.550 [74] no [216] 1.78000 2040 [233] NCBI GCA_000757845.1 ASM75784v1 scaffolds: 1 contigs: 1 N50: 1,780,061 L50: 1 https://www.ncbi.nlm.nih.gov/genome/13712?genome_assembly_id=209903 22.75000 -158.000000 [235] 0 [218]
Prochlorococcus sp. MIT0801 Prochlorococcus_sp._MIT0801-aa-gen yes Genome Prokaryote 0.350 0.350 0.550 [74] no [216] 1.93000 2171 [233] NCBI GCA_000757865.1 ASM75786v1 scaffolds: 1 contigs: 1 N50: 1,929,203 L50: 1 https://www.ncbi.nlm.nih.gov/genome/13712?genome_assembly_id=209904 31.67000 -64.170000 [233] 0 [218]
Prochlorococcus sp. MIT0602 Prochlorococcus_sp._MIT0602-aa-gen yes Genome Prokaryote 0.350 0.350 0.550 [74] no [216] 1.75092 1897 [233] NCBI GCA_000760195.1 ASM76019v1 scaffolds: 9 contigs: 9 N50: 511,704 L50: 2 https://www.ncbi.nlm.nih.gov/genome/13712?genome_assembly_id=210095 22.75000 -158.000000 [233] 0 [218]
Prochlorococcus sp. MIT0603 Prochlorococcus_sp._MIT0603-aa-gen yes Genome Prokaryote 0.350 0.350 0.550 [74] no [216] 1.75248 1896 [233] NCBI GCA_000760215.1 ASM76021v1 scaffolds: 7 contigs: 7 N50: 434,668 L50: 2 https://www.ncbi.nlm.nih.gov/genome/13712?genome_assembly_id=210096 22.75000 -158.000000 [233] 0 [218]
Prochlorococcus sp. SS52 Prochlorococcus_sp._SS52-aa-gen yes Genome Prokaryote 0.350 0.350 0.550 [74] no [216] 1.75405 1919 [233] NCBI GCA_000760375.1 ASM76037v1 scaffolds: 22 contigs: 22 N50: 124,224 L50: 5 https://www.ncbi.nlm.nih.gov/genome/13712?genome_assembly_id=210100 28.98000 -64.350000 [236] 0 [218]
Prochlorococcus sp. MIT0701 Prochlorococcus_sp._MIT0701-aa-gen yes Genome Prokaryote 0.350 0.350 0.550 [74] no [216] 2.59257 2591 [233] NCBI GCA_000760295.1 ASM76029v1 scaffolds: 53 contigs: 53 N50: 84,463 L50: 9 https://www.ncbi.nlm.nih.gov/genome/13712?genome_assembly_id=210097 -13.45000 -0.040000 [233] 0 [218]
Prochlorococcus sp. MIT0702 Prochlorococcus_sp._MIT0702-aa-gen yes Genome Prokaryote 0.350 0.350 0.550 [74] no [216] 2.58306 5288 [233] NCBI GCA_000760315.1 ASM76031v1 scaffolds: 61 contigs: 61 N50: 76,101 L50: 10 https://www.ncbi.nlm.nih.gov/genome/13712?genome_assembly_id=210098 -13.45000 -0.040000 [233] 0 [218]
Prochlorococcus sp. MIT0703 Prochlorococcus_sp._MIT0703-aa-gen yes Genome Prokaryote 0.350 0.350 0.550 [74] no [216] 2.57506 2591 [233] NCBI GCA_000760335.1 ASM76033v1 scaffolds: 61 contigs: 61 N50: 81,186 L50: 10 https://www.ncbi.nlm.nih.gov/genome/13712?genome_assembly_id=210099 -13.45000 -0.040000 [233] 0 [218]
Prochlorococcus marinus SB Prochlorococcus_marinus_SB-aa-gen yes Genome Prokaryote 0.350 0.350 0.550 [74] no [216] 1.66982 1874 [233] NCBI GCA_000760115.1 ASM76011v1 scaffolds: 4 contigs: 4 N50: 1,237,529 L50: 1 https://www.ncbi.nlm.nih.gov/genome/164?genome_assembly_id=209989 35.00000 138.300000 [234] 0 [218]
Prochlorococcus marinus SS35 Prochlorococcus_marinus_SS35-aa-gen yes Genome Prokaryote 0.350 0.350 0.550 [74] no [216] 1.75101 1895 [233] NCBI GCA_000760275.1 ASM76027v1 scaffolds: 9 contigs: 9 N50: 446,270 L50: 2 https://www.ncbi.nlm.nih.gov/genome/164?genome_assembly_id=209993 28.98000 -64.350000 [236] 0 [218]
Prochlorococcus marinus SS51 Prochlorococcus_marinus_SS51-aa-gen yes Genome Prokaryote 0.350 0.350 0.550 [74] no [216] 1.74698 1908 [233] NCBI GCA_000760355.1 ASM76035v1 scaffolds: 12 contigs: 12 N50: 232,789 L50: 3 https://www.ncbi.nlm.nih.gov/genome/164?genome_assembly_id=209994 28.98000 -64.350000 [236] 0 [218]
Prochlorococcus marinus SS2 Prochlorococcus_marinus_SS2-aa-gen yes Genome Prokaryote 0.350 0.350 0.550 [74] no [216] 1.75277 1903 [233] NCBI GCA_000760255.1 ASM76025v1 scaffolds: 19 contigs: 19 N50: 187,268 L50: 3 https://www.ncbi.nlm.nih.gov/genome/164?genome_assembly_id=209992 28.98000 -64.350000 [236] 0 [218]
Prochlorococcus marinus MIT9314 Prochlorococcus_marinus_MIT9314-aa-gen yes Genome Prokaryote 0.350 0.350 0.550 [74] no [216] 1.69056 1937 [233] NCBI GCA_000760035.1 ASM76003v1 scaffolds: 16 contigs: 16 N50: 221,824 L50: 3 https://www.ncbi.nlm.nih.gov/genome/164?genome_assembly_id=209985 37.51000 -64.240000 [236] 0 [218]
Prochlorococcus marinus MIT9321 Prochlorococcus_marinus_MIT9321-aa-gen yes Genome Prokaryote 0.350 0.350 0.550 [74] no [216] 1.65866 1903 [233] NCBI GCA_000760055.1 ASM76005v1 scaffolds: 10 contigs: 10 N50: 259,210 L50: 3 https://www.ncbi.nlm.nih.gov/genome/164?genome_assembly_id=209986 1.00000 -92.000000 [236] 0 [218]
Prochlorococcus marinus MIT9322 Prochlorococcus_marinus_MIT9322-aa-gen yes Genome Prokaryote 0.350 0.350 0.550 [74] no [216] 1.65755 1901 [233] NCBI GCA_000760075.1 ASM76007v1 scaffolds: 11 contigs: 11 N50: 367,597 L50: 2 https://www.ncbi.nlm.nih.gov/genome/164?genome_assembly_id=209987 0.27000 -93.000000 [236] 0 [218]
Prochlorococcus marinus MIT9401 Prochlorococcus_marinus_MIT9401-aa-gen yes Genome Prokaryote 0.350 0.350 0.550 [74] no [216] 1.66681 1919 [233] NCBI GCA_000760095.1 ASM76009v1 scaffolds: 17 contigs: 17 N50: 110,519 L50: 4 https://www.ncbi.nlm.nih.gov/genome/164?genome_assembly_id=209988 35.50000 -70.400000 [236] 0 [218]
Prochlorococcus marinus EQPAC1 Prochlorococcus_marinus_EQPAC1-aa-gen yes Genome Prokaryote 0.350 0.350 0.550 [74] no [216] 1.65474 1907 [233] NCBI GCA_000759875.1 ASM75987v1 scaffolds: 8 contigs: 8 N50: 328,627 L50: 2 https://www.ncbi.nlm.nih.gov/genome/164?genome_assembly_id=209979 0.00000 -180.000000 [236] 0 [218]
Synechococcus sp. WH7803 Synechococcus_sp._WH7803-aa-gen yes Genome Prokaryote 0.500 0.500 0.500 [187] no [237] 2.36698 2586 NCBI GCA_000063505.1 ASM6350v1 scaffolds: 1 contigs: 1 N50: 2,366,980 L50: 1 https://www.ncbi.nlm.nih.gov/genome/13522?genome_assembly_id=174736 33.75000 -67.500000 [187] 0 [238]
Synechococcus sp. WH8101 Synechococcus_sp._WH8101-aa-gen yes Genome Prokaryote 0.500 0.500 0.500 [239] no [237] 2.63029 2816 NCBI GCA_004209775.1 ASM420977v1 scaffolds: 1 contigs: 1 N50: 2,630,292 L50: 1 https://www.ncbi.nlm.nih.gov/genome/13522?genome_assembly_id=451744 41.31410 -70.401300 [240] 0 [238]
Synechococcus sp. WH8103 Synechococcus_sp._WH8103-aa-gen yes Genome Prokaryote 0.500 0.500 0.500 [239] no [237] 2.42969 2648 NCBI GCA_001182765.1 WH8103.1 scaffolds: 1 contigs: 1 N50: 2,429,688 L50: 1 https://www.ncbi.nlm.nih.gov/genome/13522?genome_assembly_id=240763 28.50000 -67.390000 [187] 0 [238]
Synechococcus sp. WH8109 Synechococcus_sp._WH8109-aa-gen yes Genome Prokaryote 0.500 0.500 0.500 [239] no [237] 2.11151 2363 NCBI GCA_000161795.2 ASM16179v2 scaffolds: 1 contigs: 1 N50: 2,111,515 L50: 1 https://www.ncbi.nlm.nih.gov/genome/13522?genome_assembly_id=228479 39.48000 -70.460000 [187] 0 [238]
Synechococcus sp. PCC7002 Synechococcus_sp._PCC7002-aa-gen yes Genome Prokaryote 0.500 0.500 0.500 [239] no [237] 3.40993 2872 NCBI GCA_000019485.1 ASM1948v1 scaffolds: 7 contigs: 7 N50: 3,008,047 L50: 1 https://www.ncbi.nlm.nih.gov/genome/13522?genome_assembly_id=300956 0 [238]
Synechococcus sp. MEDNS5 Synechococcus_sp._MEDNS5-aa-gen yes Genome Prokaryote 0.500 0.500 0.500 [187] no [237] 2.43587 2607 NCBI GCA_014279875.1 ASM1427987v1 scaffolds: 1 contigs: 1 N50: 2,435,869 L50: 1 https://www.ncbi.nlm.nih.gov/genome/13522?genome_assembly_id=978542 41.00000 6.000000 [187] 0 [238]
Synechococcus sp. MITS9220 Synechococcus_sp._MITS9220-aa-gen yes Genome Prokaryote 0.500 0.500 0.500 [187] no [237] 2.42417 2650 NCBI GCA_014304815.1 ASM1430481v1 scaffolds: 1 contigs: 1 N50: 2,424,175 L50: 1 https://www.ncbi.nlm.nih.gov/genome/13522?genome_assembly_id=979223 0 [238]
Synechococcus sp. NIES-970 Synechococcus_sp._NIES970-aa-gen yes Genome Prokaryote 0.750 0.750 0.750 no [237] 3.12386 2685 [241] NCBI GCA_002356215.1 ASM235621v1 scaffolds: 5 contigs: 5 N50: 2,836,539 L50: 1 https://www.ncbi.nlm.nih.gov/genome/13522?genome_assembly_id=331806 27.00000 129.000000 [241] 0 [238]
Cyanobium sp. LEGE06143 Cyanobium_sp_LEGE06143-aa-gen yes Genome Prokaryote 0.600 0.600 0.600 [242] no [243] 2.57073 2780 NCBI GCA_015207535.1 ASM1520753v1 scaffolds: 317 contigs: 317 N50: 11,389 L50: 69 https://www.ncbi.nlm.nih.gov/genome/13678?genome_assembly_id=1482232 37.07156 -8.774722 [242] 0 [230]
Cyanobium sp. LEGE06113 Cyanobium_sp_LEGE06113-aa-gen yes Genome Prokaryote 0.600 0.600 0.600 [242] no [243] 2.93701 3076 NCBI GCA_015207615.1 ASM1520761v1 scaffolds: 18 contigs: 18 N50: 328,991 L50: 3 https://www.ncbi.nlm.nih.gov/genome/13678?genome_assembly_id=1482231 41.04954 -8.655339 [242] 0 [230]
Synechococcus sp. SynAce01 Synechococcus_sp._SynAce01-aa-gen yes Genome Prokaryote 0.500 0.500 0.500 [239] no [237] 2.75000 2950 JGI Synechococcus sp. SynAce01: 118502.assembled.fna https://genome.jgi.doe.gov/portal/pages/dynamicOrganismDownload.jsf?organism=IMG_2718218441 -68.27000 78.120000 0 [238]
Gephyrocapsa oceanica RCC1303 Gephyrocapsa_oceanica_RCC1303-aa-trans yes Transcriptome Haptophyte 2.300 2.300 2.300 [187] no [243] 1.00000 38577 [176] iMicrobe Gephyrocapsa-oceanica-RCC1303.pep ftp://ftp.imicrobe.us/camera/combined_assemblies/ 45.00000 -1.080000 [187] 0 [244]
Fragilariopsis cylindrus CCMP1102 Fragilariopsis_cylindrus_CCMP1102-aa-gen yes Genome Diatom Pennate [245] 1.500 1.500 2.500 [158] no [166] 80.50000 27137 [245] JGI Fragilariopsis cylindrus CCMP 1102: Project: 16035, Fracy1_GeneModels_FilteredModels1_aa.fasta.gz https://genome.jgi.doe.gov/portal/pages/dynamicOrganismDownload.jsf?organism=Fracy1 -64.08000 -48.703300 [158] 1 [246]
Fragilariopsis cylindrus CCMP1102 Fragilariopsis_cylindrus_CCMP1102-dna-trans yes Transcriptome Diatom Pennate [245] 1.500 1.500 2.500 [158] no [166] 1.00000 0 [245] JGI Fragilariopsis cylindrus CCMP 1102: Project: 16035, Fracy1_GeneModels_FilteredModels1_nt.fasta.gz https://mycocosm.jgi.doe.gov/Fracy1/Fracy1.home.html -64.08000 -48.703300 [158] 1 [246]
Pseudo-nitzschia multistriata B856 Pseudo-nitzschia_multistriata_B856-aa-gen yes Genome Diatom Pennate [247] 1.650 1.650 23.500 [247] no [247] 56.76520 12039 [248] NCBI ASM90066040v1 https://www.ncbi.nlm.nih.gov/assembly/GCA_900660405.1/ 1 [249]
Pseudo-nitzschia multiseries CLN-47 Pseudo-nitzschia_multiseries_CLN-47-aa-gen yes Genome Diatom Pennate [249] 2.250 2.250 33.300 [249] no [250] 218.73000 19703 JGI 1076539 https://genome.jgi.doe.gov/portal/PsenitmultiCLN47_FD/PsenitmultiCLN47_FD.info.html 1 [251]
Pseudo-nitzschia multiseries CLN-47 Pseudo-nitzschia_multiseries_CLN-47-dna-trans yes Transcriptome Diatom Pennate [249] 2.250 2.250 33.300 [249] no [250] 1.00000 0 JGI 1076539 https://genome.jgi.doe.gov/portal/PsenitmultiCLN47_FD/PsenitmultiCLN47_FD.info.html 1 [251]
Nitzschia palea
Nitzschia_palea-dna-trans yes Transcriptome Diatom Pennate [252] 1.750 1.750 13.500 [252] no [166] 1.00000 15885 [253] ENA GHBX01000000 https://www.ebi.ac.uk/ena/data/view/GHBX01000000 1 [254]
Leptocylindrus danicus
Leptocylindrus_danicus-dna-trans yes Transcriptome Diatom Centric [175] 5.250 5.250 35.000 [175] no [166] 1.00000 15701 [255] ENA GAUB01000000 https://www.ebi.ac.uk/ena/data/view/GAUB01000000 1 [256]
Thalassiosira antarctica CCMP982 Thalassiosira_antarctica_CCMP982-aa-trans yes Transcriptome Diatom Centric [158] 5.500 5.500 10.000 [158] no [166] 1.00000 31618 [176] iMicrobe Thalassiosira-antarctica-CCMP982.pep.fa.gz ftp://ftp.imicrobe.us/camera/combined_assemblies/ 59.50000 10.600000 [158] 1 [174]
Thalassiosira rotula CCMP3096 Thalassiosira_rotula_CCMP3096-aa-trans yes Transcriptome Diatom Centric [158] 8.750 8.750 18.500 [158] no [166] 1.00000 26935 [176] iMicrobe Thalassiosira-rotula-CCMP3096.pep.fa.gz ftp://ftp.imicrobe.us/camera/combined_assemblies/ 49.65000 -127.433800 [158] 1 [257]
Chaetoceros neogracile CCMP1317 Chaetoceros_neogracile_CCMP1317-aa-trans yes Transcriptome Diatom Centric [158] 1.750 1.750 4.750 [158] no [166] 1.00000 27860 [176] iMicrobe Chaetoceros-neogracile-CCMP1317.pep.fa.gz ftp://ftp.imicrobe.us/camera/combined_assemblies/ 32.85000 -117.350000 [158] 1 [258]
Thalassiosira minuscula CCMP1093 Thalassiosira_miniscula_CCMP1093-aa-trans yes Transcriptome Diatom Centric [158] 6.000 6.000 7.500 [158] no [166] 1.00000 44803 [176] iMicrobe Thalassiosira-minuscula-CCMP1093.pep.fa.gz ftp://ftp.imicrobe.us/camera/combined_assemblies/ 32.90000 -117.255000 [158] 1 [259]
Ditylum brightwellii GSO103 Ditylum_brightwellii_GSO103-aa-trans yes Transcriptome Diatom Centric [175] 33.500 33.500 85.000 [175] no [166] 1.00000 24623 [176] iMicrobe Ditylu-brightwellii-GSO103.pep.fa.gz ftp://ftp.imicrobe.us/camera/combined_assemblies/ 1 [260]
Chaetoceros curvisetus
Chaetoceros_curvisetus-aa-trans yes Transcriptome Diatom Centric [161] 5.750 8.750 11.250 [161] no [166] 1.00000 23542 [176] iMicrobe Chaetoceros-curvisetus .pep.fa.gz ftp://ftp.imicrobe.us/camera/combined_assemblies/ 1 [261]
(Symbiodinium) Breviolum minutum
Symbiodinium_minutum-aa-gen yes Genome Symbiodinium 3.750 3.750 3.750 [262] yes [263] 1500.00000 41925 [264] MGU symbB.v1.2.augustus.prot.fa.gz https://marinegenomics.oist.jp/symb/viewer/download?project_id=21 1 [265]
(Symbiodinium) Breviolum minutum
Symbiodinium_minutum-dna-trans yes Transcriptome Symbiodinium 3.750 3.750 3.750 [262] yes [263] 1.00000 0 [264] MGU symbB1_v1.0.transcriptome_assembly.fa.gz https://marinegenomics.oist.jp/symb/viewer/download?project_id=21 1 [265]
(Symbiodinium) Cladocopium goreaui
Symbiodinium_goreaui-aa-gen yes Genome Symbiodinium 3.350 3.350 4.150 [266] yes [213] 705.00000 65832 [264] MGU symC_aug_40.aa.gz https://marinegenomics.oist.jp/symb/viewer/download?project_id=40 1 [267] MesophyticColony
(Symbiodinium) Cladocopium goreaui
Symbiodinium_goreaui-dna-trans yes Transcriptome Symbiodinium 3.350 3.350 3.350 [266] yes [213] 1.00000 0 [264] MGU symC_transcriptome_40.fasta.gz https://marinegenomics.oist.jp/symb/viewer/download?project_id=40 1 [267] MesophyticColony
Polarella glacialis CCMP1383 Polarella_glacialis_CCMP1383-aa-gen yes Genome Dinoflagellate 3.000 3.000 6.250 [158] yes [268] 2980.00000 58232 [269] AARNET Polarella_glacialis_CCMP1383_PredGene_v1.pep.fa https://cloudstor.aarnet.edu.au/plus/s/Nx08JEMt7FjK3zY -77.83330 163.000000 [158] 1 [270]
Polarella glacialis CCMP2088 Polarella_glacialis_CCMP2088-aa-gen yes Genome Dinoflagellate 4.500 4.500 8.000 [158] yes [268] 2760.00000 51713 [269] AARNET Polarella_glacialis_CCMP2088_PredGene_v1.pep.fa https://cloudstor.aarnet.edu.au/plus/s/Nx08JEMt7FjK3zY 78.59780 -74.499200 [158] 1 [270]
Phaeocystis antarctica CCMP1374 Phaeocystis_antarctica_CCMP1374-aa-gen yes Genome Haptophyte 3.250 3.250 3.250 [271] yes [272] 198.94000 41088 JGI JGI Phaeocystis antarctica CCMP1374 v2.2: Project: 1070272, Phaant1_GeneCatalog_proteins_20181014.aa.fasta.gz https://phycocosm.jgi.doe.gov/Phaant1/Phaant1.info.html -77.83330 163.000000 [158] 1 [273]
Phaeocystis antarctica CCMP1374 Phaeocystis_antarctica_CCMP1374-dna-trans yes Transcriptome Haptophyte 3.250 3.250 3.250 [271] yes [272] 1.00000 0 JGI JGI Phaeocystis antarctica CCMP1374 v2.2: Project: 1070272, Phaant1_GeneCatalog_transcripts_20181014.nt.fasta.gz https://phycocosm.jgi.doe.gov/Phaant1/Phaant1.info.html -77.83330 163.000000 [158] 1 [273]
Phaeocystis globosa Pg-G Phaeocystis_globosa_Pg-G-aa-gen yes Genome Haptophyte 3.450 3.450 3.450 [274] yes [274] 155.85000 32196 JGI JGI Phaeocystis globosa Pg-G v2.3: Project: 1070438, Phaglo1_GeneModels_PhytozomeGeneModels_proteins_20190222.aa.fasta.gz https://mycocosm.jgi.doe.gov/Phaglo1/Phaglo1.home.html 1 [158]
Phaeocystis globosa Pg-G Phaeocystis_globosa_Pg-G-dna-trans yes Transcriptome Haptophyte 3.450 3.450 3.450 [274] yes [274] 1.00000 0 JGI JGI Phaeocystis globosa Pg-G v2.3: Project: 1070438, Phaglo1_GeneCatalog_transcripts_20181013.nt.fasta.gz https://mycocosm.jgi.doe.gov/Phaglo1/Phaglo1.home.html 1 [158]
Alexandrium temarense CCMP1771 Alexandrium_temarense_CCMP1771-aa-trans yes Transcriptome Dinoflagellate 18.750 18.750 19.500 [158] yes [213] 1.00000 135584 [176] iMicrobe Alexandrium-temarense-CCMP1771/ ftp://ftp.imicrobe.us/camera/combined_assemblies/Alexandrium-temarense-CCMP1771/ 50.36000 -4.150000 [158] 1 [275]
Amphora coffeaeformis CCMP127 Amphora_coffeaeformis_CCMP127-aa-trans yes Transcriptome Diatom Pennate [158] 3.000 3.000 9.250 [158] no [166] 1.00000 15523 [176] iMicrobe Amphora-coffeaeformis-CCMP127/ ftp://ftp.imicrobe.us/camera/combined_assemblies/Amphora-coffeaeformis-CCMP127/ 41.52500 -70.673600 [158] 1 [276]
Asterionellopsis glacialis CCMP134 Asterionellopsis_glacialis_CCMP134-aa-trans yes Transcriptome Diatom Pennate [158] 4.500 4.500 9.500 [158] no [166] 1.00000 23387 [176] iMicrobe Asterionellopsis-glacialis-CCMP134/ ftp://ftp.imicrobe.us/camera/combined_assemblies/Asterionellopsis-glacialis-CCMP134/ 32.65550 -117.140000 [158] 1 [277]
Extubocellulus spinifer CCMP396 Extubocellulus_spinifer_CCMP396-aa-trans yes Transcriptome Diatom Centric [158] 1.250 1.250 2.750 [158] no [166] 1.00000 53419 [176] iMicrobe Extubocellulus-spinifer-CCMP396/ ftp://ftp.imicrobe.us/camera/combined_assemblies/Extubocellulus-spinifer-CCMP396/ 31.31720 -113.560000 [158] 1 [278]
Skeletonema menzelii CCMP793 Skeletonema_menzelii_CCMP793-aa-trans yes Transcriptome Diatom Centric [158] 2.750 2.750 3.000 [158] no [166] 1.00000 16088 [176] iMicrobe Skeletonema-menzelii-CCMP793/ ftp://ftp.imicrobe.us/camera/combined_assemblies/Skeletonema-menzelii-CCMP793/ 41.56600 -70.584200 [158] 1 [158]
Cyanobium sp. PCC7001 Cyanobium_sp_PCC7001-aa-gen yes Genome Prokaryote 0.380 0.380 0.650 [279] no [279] 2.83000 2725 NCBI NCBI GCA_000155635.1 https://www.ncbi.nlm.nih.gov/genome/13678?genome_assembly_id=175233 1 [230]
Prasinoderma coloniale CCMP1413 Prasinoderma_coloniale_CCMP1413-aa-gen yes Genome Green 1.250 1.250 1.250 [158] no 25.32000 7139 JGI JGI Prasinoderma coloniale CCMP1413: Praco1_ExternalModels_proteins_20200803.aa.fasta.gz https://genome.jgi.doe.gov/portal/pages/dynamicOrganismDownload.jsf?organism=Praco1 35.00000 -70.000000 [158] 1 [280]
Nitzschia frigida
Nitzschia_frigida-dna-trans yes Transcriptome Diatom Pennate [281] 2.250 20.000 20.000 [282] no [166] 1.00000 0 [283] Rokitta 2019 N.frigida contigs.fasta https://zenodo.org/record/3361258#.YD1Uj5NKjDI 1 [284]
Thalassiosira hyalina
Thalassiosira_hyalina-dna-trans yes Transcriptome Diatom Centric [281] 9.500 9.500 16.000 [282] no [166] 1.00000 0 [283] Rokitta 2019 T.hyalina contigs.fasta https://zenodo.org/record/3361258#.YD1Uj5NKjDI 1 [285]
Crocosphaera watsonii WH8501 Crocosphaera_watsonii_WH8501-aa-gen yes Genome Prokaryote 1.500 1.500 1.500 [286] no [243] 6.24000 6302 NCBI [287] NCBI GCF_000167195.1_ASM16719v1_protein.faa https://www.ncbi.nlm.nih.gov/genome/1130?genome_assembly_id=170600 -28.00000 -48.000000 [286] 1 [288]
Crocosphaera watsonii WH0401 Crocosphaera_watsonii_WH0401-aa-gen yes Genome Prokaryote 1.470 1.470 1.470 [286] no [243] 4.55000 4718 NCBI NCBI GCF_001039615.1_WH0401_v1_protein.faa https://www.ncbi.nlm.nih.gov/genome/1130?genome_assembly_id=233292 6.00000 -49.000000 [286] 1 [288]
Crocosphaera watsonii WH0003 Crocosphaera_watsonii_WH0003-aa-gen yes Genome Prokaryote 2.540 2.540 2.540 [286] no [243] 5.89000 5877 NCBI [287] NCBI GCA_000235665.2 ASM23566v2 https://www.ncbi.nlm.nih.gov/genome/1130?genome_assembly_id=170601 22.00000 -158.000000 [286] 1 [288]
Crocosphaera watsonii WH0402 Crocosphaera_watsonii_WH0402-aa-gen yes Genome Prokaryote 2.280 2.280 2.280 [286] no [243] 5.88000 5963 NCBI NCBI GCA_001039635.1 WH0402_v1 https://www.ncbi.nlm.nih.gov/genome/1130?genome_assembly_id=233293 -11.00000 -32.000000 [286] 1 [288]
Crocosphaera watsonii WH0005 Crocosphaera_watsonii_WH0005-aa-gen yes Genome Prokaryote 2.150 2.150 2.150 [286] no [243] 5.96000 6029 NCBI NCBI GCA_001050835.1 ASM105083v1 https://www.ncbi.nlm.nih.gov/genome/1130?genome_assembly_id=234839 21.00000 -157.000000 [286] 1 [288]
Crocosphaera subtropica ATCC51142 Crocosphaera_subtropica_ATCC51142-aa-gen yes Genome Prokaryote 2.250 2.250 2.250 [289] no [243] 5.46038 5063 [290] NCBI GCA_000017845.1 ASM1784v1 scaffolds: 6 contigs: 6 N50: 4,934,271 L50: 1 https://www.ncbi.nlm.nih.gov/genome/?term=txid43989[Organism:noexp] 27.78000 -97.110000 1 [291]
Fragilariopsis kerguelensis L2_C3 Fragilariopsis_kerguelensis_L2_C3-aa-trans yes Transcriptome Diatom Pennate [292] 3.900 3.900 15.365 [293] no [166] 1.00000 61393 [176] iMicrobe Fragilariopsis-kerguelensis-L2_C3.pep ftp://ftp.imicrobe.us/camera/combined_assemblies/ 1 [294]
Pseudo-nitzschia fradulenta WWA7 Pseudo-nitzschia_fradulenta_WWA7-aa-trans yes Transcriptome Diatom Pennate [249] 3.000 3.000 57.250 [295] no [166] 1.00000 51324 [176] iMicrobe Pseudo_nitzschia-fradulenta-WWA7.pep ftp://ftp.imicrobe.us/camera/combined_assemblies/ 1 [296]
Pseudo-nitzschia australis 10249_10_AB Pseudo-nitzschia_australis_10249_10_AB-aa-trans yes Transcriptome Diatom Pennate [249] 3.325 3.325 51.500 [295] no [166] 1.00000 21468 [176] iMicrobe Pseudo_nitzschia-australis-10249_10_AB.pep ftp://ftp.imicrobe.us/camera/combined_assemblies/ 1 [297] Another article: https://www.tandfonline.com/doi/pdf/10.2216/11-37.1?needAccess=true
Thalassiothrix antarctica L6_D1 Thalassiothrix_antarctica_L6_D1-aa-trans yes Transcriptome Diatom Pennate [298] 2.750 2.750 680.000 [299] no [166] 1.00000 26469 [176] iMicrobe Thalassiothrix-antarctica-L6_D1.pep ftp://ftp.imicrobe.us/camera/combined_assemblies/ 1 [300]
Skeletonema marinoi SkelA Skeletonema_marinoi_SkelA-aa-trans yes Transcriptome Diatom Centric [301] 3.950 3.950 5.975 [302] no [302] 1.00000 21103 [176] iMicrobe Skeletonema-marinoi-SkelA.pep ftp://ftp.imicrobe.us/camera/combined_assemblies/ 1 [303]
Skeletonema dohrnii SkelB Skeletonema_dohrnii_SkelB-aa-trans yes Transcriptome Diatom Centric [158] 2.250 2.250 16.000 [304] no [304] 1.00000 23719 [176] iMicrobe Skeletonema-dohrnii-SkelB.pep ftp://ftp.imicrobe.us/camera/combined_assemblies/ 1 [305]
Symbiodinium microadriaticum CCMP2467 Symbiodinium_microadriaticum_CCMP2467-aa-gen yes Genome Symbiodinium 4.250 4.250 4.250 [158] yes [306] 808.23000 43403 [307] JGI Symbiodinium microadriaticum CCMP2467 https://mycocosm.jgi.doe.gov/Symmic1/Symmic1.home.html 29.00000 34.750000 [158]
(Symbiodinium) Fugacium kawagutii
Symbiodinium_kawagutii-aa-gen yes Genome Symbiodinium 4.250 4.250 4.250 [158] yes [213] 1180.00000 36850 [308] MALAB Sequences: proteins http://web.malab.cn/symka_new/download.jsp
Symbiodinium tridacnidorum
Symbiodinium_tridacnidorum-aa-gen yes Genome Symbiodinium 4.550 4.550 5.550 [@ leeSymbiodiniumTridacnidorumSp2015] yes [309] 766.60000 69018 [264] MGU symA3_37.fasta.gz https://marinegenomics.oist.jp/symb/viewer/download?project_id=37
Symbiodinium tridacnidorum
Symbiodinium_tridacnidorum-dna-trans yes Transcriptome Symbiodinium 4.550 4.550 5.550 [@ leeSymbiodiniumTridacnidorumSp2015] yes [309] 1.00000 0 [264] MGU syma_transcriptome_37.fasta.gz https://marinegenomics.oist.jp/symb/viewer/download?project_id=37
Ostreococcus lucimarinus CCE9901 Ostreococcus_lucimarinus_CCE9901-aa-gen yes Genome Green 0.500 0.500 1.250 [158] no [188] 13.20000 7651 [188] JGI 1077109 https://genome.jgi.doe.gov/portal/Ostnus_FD/Ostnus_FD.info.html 32.90000 -117.255000 [188] 0 [187]
Ostreococcus lucimarinus CCE9901 Ostreococcus_lucimarinus_CCE9901-dna-trans yes Transcriptome Green 0.500 0.500 1.250 [158] no [188] 1.00000 0 [188] JGI 1077109 https://genome.jgi.doe.gov/portal/Ostnus_FD/Ostnus_FD.info.html 32.90000 -117.255000 [188] 0 [187]
Ostreococcus sp. RCC809 Ostreococcus_sp_RCC809-aa-gen yes Genome Green 1.000 1.000 1.000 [187] no [188] 13.30000 7492 JGI 16233 https://mycocosm.jgi.doe.gov/OstRCC809_2/OstRCC809_2.home.html 21.03000 -31.130000 [187] 0 [187]
Ostreococcus sp. RCC809 Ostreococcus_sp_RCC809-dna-trans yes Transcriptome Green 1.000 1.000 1.000 [187] no [188] 1.00000 0 JGI 16233 https://mycocosm.jgi.doe.gov/OstRCC809_2/OstRCC809_2.home.html 21.03000 -31.130000 [187] 0 [187]
Micromonas commoda RCC299 Micromonas_commoda_RCC299-aa-gen yes Genome Green 1.000 1.000 1.000 [187] yes [310] 21.10930 10137 [194] NCBI GCA_000090985.2 https://bioinformatics.psb.ugent.be/plaza/versions/pico-plaza/download -22.33000 166.330000 [187] 0 [311]
Micromonas commoda RCC299 Micromonas_commoda_RCC299-dna-trans yes Transcriptome Green 1.000 1.000 1.000 [187] yes [310] 1.00000 0 [194] NCBI GCA_000090985.2 https://bioinformatics.psb.ugent.be/plaza/versions/pico-plaza/download -22.33000 166.330000 [187] 0 [311]
Pavlovales sp. CCMP2436 Pavlovales_sp_CCMP2436-aa-gen yes Genome Haptophyte 3.750 3.750 5.000 [158] yes https://phycocosm.jgi.doe.gov/Pavlov2436_1/Pavlov2436_1.home.html 165.41000 26034 JGI Pavlovales sp. CCMP2436 v1.0: Project: 1014533, Pavlov2436_1_GeneCatalog_proteins_20160817.aa.fasta.gz https://genome.jgi.doe.gov/portal/pages/dynamicOrganismDownload.jsf?organism=Pavlov2436_1 76.32080 -75.817200 [158]
Pavlovales sp. CCMP2436 Pavlovales_sp_CCMP2436-dna-trans yes Transcriptome Haptophyte 3.750 3.750 5.000 [158] yes https://phycocosm.jgi.doe.gov/Pavlov2436_1/Pavlov2436_1.home.html 1.00000 0 JGI Pavlovales sp. CCMP2436 v1.0: Project: 1014533, Pavlov2436_1_GeneCatalog_transcripts_20160817.nt.fasta.gz https://genome.jgi.doe.gov/portal/pages/dynamicOrganismDownload.jsf?organism=Pavlov2436_1 76.32080 -75.817200 [158]
Ochromonadaceae sp. CCMP2298 Ochromonadaceae_sp_CCMP2298-aa-gen yes Genome Chrysophyte 5.750 5.750 5.750 [158] yes https://phycocosm.jgi.doe.gov/Ochro2298_1/Ochro2298_1.home.html 61.40000 20195 [312] JGI Ochromonadaceae sp. CCMP2298 v1.0 https://genome.jgi.doe.gov/portal/pages/dynamicOrganismDownload.jsf?organism=Ochro2298_1 77.00140 -77.238300 [158]
Ochromonadaceae sp. CCMP2298 Ochromonadaceae_sp_CCMP2298-dna-trans yes Transcriptome Chrysophyte 5.750 5.750 5.750 [158] yes https://phycocosm.jgi.doe.gov/Ochro2298_1/Ochro2298_1.home.html 1.00000 0 [312] JGI Ochromonadaceae sp. CCMP2298 v1.0 https://genome.jgi.doe.gov/portal/pages/dynamicOrganismDownload.jsf?organism=Ochro2298_1 77.00140 -77.238300 [158]
Pelagomonas calceolata CCMP1756 Pelagomonas_calceolata_CCMP1756-aa-trans yes Transcriptome Pelagophyte 1.000 1.000 1.500 [158] yes [198] 1.00000 19184 [176] iMicrobe Pelagomonas-calceolata-CCMP1756.pep.fa.gz ftp://ftp.imicrobe.us/camera/combined_assemblies/ 30.83330 -136.833300 [158] 0 [243]
Chrysochromulina polylepis CCMP1757 Chrysochromulina_polylepis_CCMP1757-aa-trans yes Transcriptome Haptophyte 2.750 2.750 3.500 [158] yes 1.00000 0 [176] iMicrobe Chrysochromulina-polylepis-CCMP1757/ ftp://ftp.imicrobe.us/camera/combined_assemblies/Chrysochromulina-polylepis-CCMP1757/ 64.75000 21.333300 [158]
Dunaliella tertiolecta CCMP1320 Dunaliella_tertiolecta_CCMP1320-aa-trans yes Transcriptome Green 3.750 3.750 3.750 [158] yes [226] 1.00000 17597 [176] iMicrobe Dunaliella-tertiolecta-CCMP1320/ ftp://ftp.imicrobe.us/camera/combined_assemblies/Dunaliella-tertiolecta-CCMP1320/
Goniomonas pacifica CCMP1869 Goniomonas_pacifica_CCMP1869-aa-trans yes Transcriptome Cryptophyte 2.500 2.500 3.750 [158] yes 1.00000 57820 [176] iMicrobe Goniomonas-pacifica-CCMP1869/ ftp://ftp.imicrobe.us/camera/combined_assemblies/Goniomonas-pacifica-CCMP1869/ -37.86000 144.930000 [158]
Karenia brevis CCMP2229 Karenia_brevis_CCMP2229-aa-trans yes Transcriptome Dinoflagellate 11.000 11.000 12.000 [158] yes [213] 1.00000 95332 [176] iMicrobe Karenia-brevis-CCMP2229/ ftp://ftp.imicrobe.us/camera/combined_assemblies/Karenia-brevis-CCMP2229/ 27.01710 -82.476300 [158] [313]
Karlodinium micrum CCMP2283 Karlodinium_micrum_CCMP2283-aa-trans yes Transcriptome Dinoflagellate 4.500 4.500 6.500 [158] yes [213] 1.00000 72309 [176] iMicrobe Karlodinium-micrum-CCMP2283/ ftp://ftp.imicrobe.us/camera/combined_assemblies/Karlodinium-micrum-CCMP2283/ 32.21670 -80.735500 [158]
Micromonas polaris CCMP2099 Micromonas-polaris-CCMP2099-aa-trans yes Transcriptome Green 1.000 1.000 1.250 [158] yes 1.00000 10163 [176] iMicrobe Micromonas-sp-CCMP2099/ ftp://ftp.imicrobe.us/camera/combined_assemblies/Micromonas-sp-CCMP2099/ 76.28330 -74.750000 [158] 0 [187]
Nitzschia punctata CCMP561 Nitzschia_punctata_CCMP561-aa-trans yes Transcriptome Diatom Pennate [158] 2.750 2.750 3.500 [158] no [166] 1.00000 21173 [176] iMicrobe Nitzschia-punctata-CCMP561/ ftp://ftp.imicrobe.us/camera/combined_assemblies/Nitzschia-punctata-CCMP561/ 32.99000 -117.255000 [158]
Ochromonas sp. CCMP1393 Ochromonas_sp_CCMP1393-aa-gen yes Genome Chrysophyte 2.500 2.500 2.500 [158] yes 56.49000 19713 JGI Ochromonas sp. CCMP1393 v1.4: Project: 1070490, Ochro1393_1_4_GeneCatalog_proteins_20181204.aa.fasta.gz  https://genome.jgi.doe.gov/portal/pages/dynamicOrganismDownload.jsf?organism=Ochro1393_1_4 38.70200 -72.366700 [158]
Ochromonas sp. CCMP1393 Ochromonas_sp_CCMP1393-aa-trans yes Transcriptome Chrysophyte 2.500 2.500 2.500 [158] yes 1.00000 21479 [176] iMicrobe Ochromonas-sp-CCMP1393/ ftp://ftp.imicrobe.us/camera/combined_assemblies/Ochromonas-sp-CCMP1393/ 38.70200 -72.366700 [158]
Pavlova sp. CCMP459 Pavlova_sp_CCMP459-aa-trans yes Transcriptome Haptophyte 2.250 2.250 3.000 [158] yes 1.00000 19266 [176] iMicrobe Pavlova-sp-CCMP459/ ftp://ftp.imicrobe.us/camera/combined_assemblies/Pavlova-sp-CCMP459/ 38.70200 -72.366700 [158] 0 [187]
Pleurochrysis carterae CCMP645 Pleurochrysis_carterae_CCMP645-aa-trans yes Transcriptome Haptophyte 5.000 5.000 5.000 [158] no 1.00000 39174 [176] iMicrobe Pleurochrysis-carterae-CCMP645/ ftp://ftp.imicrobe.us/camera/combined_assemblies/Pleurochrysis-carterae-CCMP645/ 41.52500 -70.673600 [158]
Prorocentrum minimum CCMP1329 Prorocentrum_minimum_CCMP1329-aa-trans yes Transcriptome Dinoflagellate 5.500 5.500 65.750 [158] yes [213] 1.00000 96684 [176] iMicrobe Prorocentrum-minimum-CCMP1329/ ftp://ftp.imicrobe.us/camera/combined_assemblies/Prorocentrum-minimum-CCMP1329/ 40.66670 -73.250000 [158] 0 [187]
Prorocentrum minimum CCMP2233 Prorocentrum_minimum_CCMP2233-aa-trans yes Transcriptome Dinoflagellate 9.000 9.000 9.500 [158] yes [213] 1.00000 88621 [176] iMicrobe Prorocentrum-minimum-CCMP2233/ ftp://ftp.imicrobe.us/camera/combined_assemblies/Prorocentrum-minimum-CCMP2233/ 38.59000 -75.100000 [158] 0 [187]
Rhodella maculata CCMP736 Rhodella_maculata_CCMP736-aa-trans yes Transcriptome Red 5.750 5.750 5.750 [158] no 1.00000 20890 [176] iMicrobe Rhodella-maculata-CCMP736/ ftp://ftp.imicrobe.us/camera/combined_assemblies/Rhodella-maculata-CCMP736/ 51.53200 0.700000 [158]
Paraphysomonas imperforata CCMP1604 Paraphysomonas_imperforata_CCMP1604-aa-gen yes Genome Chrysophyte 3.250 3.250 3.250 [158] yes [314] 67.19000 17326 JGI JGI Paraphysomonas imperforata CCMP1604 v1.4: Project: 1011391, Parimp1_4_GeneCatalog_proteins_20180208.aa.fasta.gz https://phycocosm.jgi.doe.gov/Parimp1_4/Parimp1_4.home.html 22.75000 -158.000000 [158]
Paraphysomonas imperforata CCMP1604 Paraphysomonas_imperforata_CCMP1604-dna-trans yes Transcriptome Chrysophyte 3.250 3.250 3.250 [158] yes [314] 1.00000 0 JGI JGI Paraphysomonas imperforata CCMP1604 v1.4: Project: 1011391, Parimp1_4_GeneCatalog_transcripts_20180208.nt.fasta.gz https://phycocosm.jgi.doe.gov/Parimp1_4/Parimp1_4.home.html 22.75000 -158.000000 [158]
Chattonella antiqua
Chattonella_antiqua-aa-trans yes Transcriptome Raphidophyte 14.000 14.000 48.000 [315] no [315] 1.00000 39031 [316] NIBB Translated contigs http://hab.nibb.ac.jp/
Heterocapsa circularisquama
Heterocapsa_circularisquama-aa-trans yes Transcriptome Dinoflagellate 8.650 8.650 11.950 [317] yes [213] 1.00000 71125 [316] NIBB Translated contigs http://hab.nibb.ac.jp/
Seminavis robusta D6 Seminavis_robusta_D6-aa-gen yes Genome Diatom Pennate [318] 3.000 3.000 15.000 [318] no [318] 125.57000 37455 JGI [319] JGI Seminavis robusta D6 Seminavis robusta D6: Semro1_GeneCatalog_proteins_20200811.aa.fasta.gz
Baffinella sp. CCMP2293 Cryptophyceae_sp_CCMP2293-aa-gen yes Genome Cryptophyte 2.250 2.250 4.500 [158] yes 534.37000 33051 [312] JGI Cryptophyceae sp. CCMP2293 v1.0 https://genome.jgi.doe.gov/portal/pages/dynamicOrganismDownload.jsf?organism=Crypto2293_1 78.59220 -74.492200 [158]
Baffinella sp. CCMP2293 Cryptophyceae_sp_CCMP2293-dna-trans yes Transcriptome Cryptophyte 2.250 2.250 4.500 [158] yes 1.00000 0 [312] JGI Cryptophyceae sp. CCMP2293 v1.0 https://genome.jgi.doe.gov/portal/pages/dynamicOrganismDownload.jsf?organism=Crypto2293_1 78.59220 -74.492200 [158]
Nitzschia inconspicua GAI-293 Nitzschia_inconspicua_GAI293-aa-gen yes Genome Diatom Pennate [185] 2.000 2.000 3.750 [185] no [166] 99.71000 38785 [185] NCBI GCA_019154785.2 https://www.ncbi.nlm.nih.gov/genome/104419
Nitzschia inconspicua GAI-293 Nitzschia_inconspicua_GAI293-dna-trans yes Transcriptome Diatom Pennate [185] 2.000 2.000 3.750 [185] no [166] 1.00000 0 [185] JGI Nitzschia inconspicua GAI-293 v2.0: Nithil2_ExternalModels_transcripts_20201125.nt.fasta.gz https://genome.jgi.doe.gov/portal/pages/dynamicOrganismDownload.jsf?organism=Nithil2
Kryptoperidinium foliaceum CCMP1326 Kryptoperidinium_foliaceum_CCMP1326-aa-trans yes Transcriptome Dinoflagellate 12.250 12.250 13.250 [320] yes [213] 1.00000 172694 [176] iMicrobe Kryptoperidinium-foliaceum-CCMP1326.pep.fa ftp://ftp.imicrobe.us/camera/combined_assemblies/ 32.83000 -117.270000 [321]
Thalassionema nitzschioides L26_B Thalassionema_nitzschioides_L26_B-aa-trans yes Transcriptome Diatom Pennate [175] 2.500 2.750 30.000 [175] no [166] 1.00000 19904 [176] iMicrobe Thalassionema-nitzschioides-L26_B.pep ftp://ftp.imicrobe.us/camera/combined_assemblies/ 1 [300]
Picochlorum sp. SENEW3 Picochlorum_sp._SENEW3-aa-gen yes Genome Green 1.250 1.250 1.250 [322] no 13.47000 7014 [323] Cyanophora Picochlorum_SENEW3_v2.0 http://cyanophora.rutgers.edu/picochlorum/ 33.00000 -117.270000 0 [187] Also known as Picochlorum SE3
Picochlorum oculata UTEXLB1998 Picochlorum_oculata_UTEXLB1998-aa-gen yes Genome Green 1.000 1.000 1.000 [324] no [324] 14.52000 6340 [323] Cyanophora Picochlorum_oculata http://cyanophora.rutgers.edu/picochlorum/ 37.24000 -76.500000
Picochlorum sp. NBRC102739 Picochlorum_sp._NBRC102739-aa-gen yes Genome Green 1.250 1.250 1.250 [322] no 22.76000 12018 [323] Cyanophora Picochlorum_NBRC102739 http://cyanophora.rutgers.edu/picochlorum/ 0 [187] Formerly known as Picochlorum (Nannochloris) MBIC10091
Diacronema lutheri NIVA-4/92 Diacronema_lutheri_NIVA492-aa-gen yes Genome Haptophyte 2.500 2.500 2.500 [325] yes [325] 30.88910 14446 [325] NCBI MSCA_Paluth_1.1 https://www.ncbi.nlm.nih.gov/genome/97098 59.29000 10.569000
Chaetoceros tenuissimus NIES-3715 Chaetoceros_tenuissimus_NIES3715-aa-gen yes Genome Diatom Centric 2.500 2.500 2.500 [326] no 41.15380 18866 [326] NCBI Cten210_1.0 https://www.ncbi.nlm.nih.gov/genome/110089 34.62889 135.035250 0
Vitrella brassicaformis CCMP3155 Vitrella_brassicaformis_CCMP3155-aa-gen yes Genome Chromerid 12.500 12.500 12.500 [158] yes [327] 72.70000 23034 [328] NCBI GCA_001179505.1 https://www.ncbi.nlm.nih.gov/genome/13390 -23.50000 152.000000 [158] 1 [329]
Chromera velia CCMP2878 Chromera_velia_CCMP2878-aa-gen yes Genome Chromerid 3.000 3.000 3.000 [158] yes https://www.nies.go.jp/chiiki1/protoz/morpho/flagella/chromera.htm 193.89000 24133 [328] JGI Chromera velia CCMP2878: Chrveli1_GeneCatalog_proteins_20200809.aa.fasta.gz https://genome.jgi.doe.gov/portal/pages/dynamicOrganismDownload.jsf?organism=Chrveli1 -33.84410 151.278900 [158]
Chloropicon primus CCMP1205 Chloropicon_primus_CCMP1205-aa-gen yes Genome Green 2.000 2.000 2.000 [158] no [330] 17.40000 8327 [330] JGI Chloropicon primus CCMP1205: Chlpri1_GeneCatalog_genes_20200307.gff.gz https://genome.jgi.doe.gov/portal/pages/dynamicOrganismDownload.jsf?organism=Chlpri1
Table 5: Comparison of Manual and Automated Gene Counts
Ome Genus species ProdOrScav ROSEnzymeName ROSEnzymeAbbrev Manual_count Automated_count
Fragilariopsis_cylindrus_CCMP1102-aa-gen Fragilariopsis cylindrus Scavenging Ascorbate peroxidase APX 4 4
Fragilariopsis_cylindrus_CCMP1102-aa-gen Fragilariopsis cylindrus Scavenging Catalase CAT 0 0
Fragilariopsis_cylindrus_CCMP1102-aa-gen Fragilariopsis cylindrus Scavenging Cytochrome C peroxidase CCP 2 3
Fragilariopsis_cylindrus_CCMP1102-aa-gen Fragilariopsis cylindrus Scavenging Dehydroascorbate reductase DHAR 1 0
Fragilariopsis_cylindrus_CCMP1102-aa-gen Fragilariopsis cylindrus Scavenging Glutathione peroxidase GPx 4 6
Fragilariopsis_cylindrus_CCMP1102-aa-gen Fragilariopsis cylindrus Scavenging Glutathione reductase GR 2 1
Fragilariopsis_cylindrus_CCMP1102-aa-gen Fragilariopsis cylindrus Scavenging Catalase peroxidase Katg 2 2
Fragilariopsis_cylindrus_CCMP1102-aa-gen Fragilariopsis cylindrus Scavenging Monodehydroascorbate reductase MDHAR 0 0
Fragilariopsis_cylindrus_CCMP1102-aa-gen Fragilariopsis cylindrus Scavenging Peroxiredoxin Prxns 7 6
Fragilariopsis_cylindrus_CCMP1102-aa-gen Fragilariopsis cylindrus Scavenging Superoxide Dismutase (Fe, Mn, Cu/Zn, Ni) SOD 3 5
Guillardia_theta_CCMP2712-aa-gen Guillardia theta Scavenging Ascorbate peroxidase APX 4 3
Guillardia_theta_CCMP2712-aa-gen Guillardia theta Scavenging Catalase CAT 0 1
Guillardia_theta_CCMP2712-aa-gen Guillardia theta Scavenging Cytochrome C peroxidase CCP 2 4
Guillardia_theta_CCMP2712-aa-gen Guillardia theta Scavenging Dehydroascorbate reductase DHAR 1 3
Guillardia_theta_CCMP2712-aa-gen Guillardia theta Scavenging Glutathione peroxidase GPx 3 3
Guillardia_theta_CCMP2712-aa-gen Guillardia theta Scavenging Glutathione reductase GR 2 0
Guillardia_theta_CCMP2712-aa-gen Guillardia theta Scavenging Catalase peroxidase Katg 1 0
Guillardia_theta_CCMP2712-aa-gen Guillardia theta Scavenging Monodehydroascorbate reductase MDHAR 0 0
Guillardia_theta_CCMP2712-aa-gen Guillardia theta Scavenging Peroxiredoxin Prxns 8 4
Guillardia_theta_CCMP2712-aa-gen Guillardia theta Scavenging Superoxide Dismutase (Fe, Mn, Cu/Zn, Ni) SOD 7 6
Phaeodactylum_tricornutum_CCAP10551-aa-gen Phaeodactylum tricornutum Scavenging Ascorbate peroxidase APX 5 4
Phaeodactylum_tricornutum_CCAP10551-aa-gen Phaeodactylum tricornutum Scavenging Catalase CAT 1 1
Phaeodactylum_tricornutum_CCAP10551-aa-gen Phaeodactylum tricornutum Scavenging Cytochrome C peroxidase CCP 4 2
Phaeodactylum_tricornutum_CCAP10551-aa-gen Phaeodactylum tricornutum Scavenging Dehydroascorbate reductase DHAR 1 0
Phaeodactylum_tricornutum_CCAP10551-aa-gen Phaeodactylum tricornutum Scavenging Glutathione peroxidase GPx 3 3
Phaeodactylum_tricornutum_CCAP10551-aa-gen Phaeodactylum tricornutum Scavenging Glutathione reductase GR 2 2
Phaeodactylum_tricornutum_CCAP10551-aa-gen Phaeodactylum tricornutum Scavenging Catalase peroxidase Katg 1 1
Phaeodactylum_tricornutum_CCAP10551-aa-gen Phaeodactylum tricornutum Scavenging Monodehydroascorbate reductase MDHAR 0 0
Phaeodactylum_tricornutum_CCAP10551-aa-gen Phaeodactylum tricornutum Scavenging Peroxiredoxin Prxns 8 4
Phaeodactylum_tricornutum_CCAP10551-aa-gen Phaeodactylum tricornutum Scavenging Superoxide Dismutase (Fe, Mn, Cu/Zn, Ni) SOD 3 3
Polarella_glacialis_CCMP1383-aa-gen Polarella glacialis Scavenging Ascorbate peroxidase APX 18 17
Polarella_glacialis_CCMP1383-aa-gen Polarella glacialis Scavenging Catalase CAT 0 0
Polarella_glacialis_CCMP1383-aa-gen Polarella glacialis Scavenging Cytochrome C peroxidase CCP 4 4
Polarella_glacialis_CCMP1383-aa-gen Polarella glacialis Scavenging Dehydroascorbate reductase DHAR 0 0
Polarella_glacialis_CCMP1383-aa-gen Polarella glacialis Production Glycolate oxidase GlyOx 0 0
Polarella_glacialis_CCMP1383-aa-gen Polarella glacialis Scavenging Glutathione peroxidase GPx 5 4
Polarella_glacialis_CCMP1383-aa-gen Polarella glacialis Scavenging Glutathione reductase GR 3 2
Polarella_glacialis_CCMP1383-aa-gen Polarella glacialis Scavenging Catalase peroxidase Katg 7 6
Polarella_glacialis_CCMP1383-aa-gen Polarella glacialis Scavenging Monodehydroascorbate reductase MDHAR 0 0
Polarella_glacialis_CCMP1383-aa-gen Polarella glacialis Production Nitric oxide synthase NOS 0 1
Polarella_glacialis_CCMP1383-aa-gen Polarella glacialis Scavenging Peroxiredoxin Prxns 15 13
Polarella_glacialis_CCMP1383-aa-gen Polarella glacialis Scavenging Superoxide Dismutase (Fe, Mn, Cu/Zn, Ni) SOD 21 19
Polarella_glacialis_CCMP1383-aa-gen Polarella glacialis Production Sulfite oxidase SulOx 4 4
Polarella_glacialis_CCMP2088-aa-gen Polarella glacialis Scavenging Ascorbate peroxidase APX 12 12
Polarella_glacialis_CCMP2088-aa-gen Polarella glacialis Scavenging Catalase CAT 0 0
Polarella_glacialis_CCMP2088-aa-gen Polarella glacialis Scavenging Cytochrome C peroxidase CCP 4 4
Polarella_glacialis_CCMP2088-aa-gen Polarella glacialis Scavenging Dehydroascorbate reductase DHAR 0 0
Polarella_glacialis_CCMP2088-aa-gen Polarella glacialis Scavenging Glutathione peroxidase GPx 5 4
Polarella_glacialis_CCMP2088-aa-gen Polarella glacialis Scavenging Glutathione reductase GR 4 4
Polarella_glacialis_CCMP2088-aa-gen Polarella glacialis Scavenging Catalase peroxidase Katg 7 5
Polarella_glacialis_CCMP2088-aa-gen Polarella glacialis Scavenging Monodehydroascorbate reductase MDHAR 0 0
Polarella_glacialis_CCMP2088-aa-gen Polarella glacialis Scavenging Peroxiredoxin Prxns 18 13
Polarella_glacialis_CCMP2088-aa-gen Polarella glacialis Scavenging Superoxide Dismutase (Fe, Mn, Cu/Zn, Ni) SOD 14 14
Pseudo-nitzschia_multistriata_B856-aa-gen Pseudo-nitzschia multistriata Scavenging Ascorbate peroxidase APX 4 1
Pseudo-nitzschia_multistriata_B856-aa-gen Pseudo-nitzschia multistriata Scavenging Catalase CAT 0 0
Pseudo-nitzschia_multistriata_B856-aa-gen Pseudo-nitzschia multistriata Scavenging Cytochrome C peroxidase CCP 1 1
Pseudo-nitzschia_multistriata_B856-aa-gen Pseudo-nitzschia multistriata Scavenging Dehydroascorbate reductase DHAR 1 0
Pseudo-nitzschia_multistriata_B856-aa-gen Pseudo-nitzschia multistriata Production Glycolate oxidase GlyOx 2 0
Pseudo-nitzschia_multistriata_B856-aa-gen Pseudo-nitzschia multistriata Scavenging Glutathione peroxidase GPx 0 0
Pseudo-nitzschia_multistriata_B856-aa-gen Pseudo-nitzschia multistriata Scavenging Glutathione reductase GR 3 1
Pseudo-nitzschia_multistriata_B856-aa-gen Pseudo-nitzschia multistriata Scavenging Catalase peroxidase Katg 1 0
Pseudo-nitzschia_multistriata_B856-aa-gen Pseudo-nitzschia multistriata Production Nitric oxide synthase NOS 0 0
Pseudo-nitzschia_multistriata_B856-aa-gen Pseudo-nitzschia multistriata Scavenging Peroxiredoxin Prxns 7 3
Pseudo-nitzschia_multistriata_B856-aa-gen Pseudo-nitzschia multistriata Scavenging Superoxide Dismutase (Fe, Mn, Cu/Zn, Ni) SOD 3 5
Pseudo-nitzschia_multistriata_B856-aa-gen Pseudo-nitzschia multistriata Production Sulfite oxidase SulOx 1 1
Symbiodinium_goreaui-aa-gen (Symbiodinium) Cladocopium goreaui Scavenging Ascorbate peroxidase APX 11 7
Symbiodinium_goreaui-aa-gen (Symbiodinium) Cladocopium goreaui Scavenging Catalase CAT 0 0
Symbiodinium_goreaui-aa-gen (Symbiodinium) Cladocopium goreaui Scavenging Cytochrome C peroxidase CCP 3 2
Symbiodinium_goreaui-aa-gen (Symbiodinium) Cladocopium goreaui Scavenging Dehydroascorbate reductase DHAR 1 2
Symbiodinium_goreaui-aa-gen (Symbiodinium) Cladocopium goreaui Scavenging Glutathione peroxidase GPx 4 2
Symbiodinium_goreaui-aa-gen (Symbiodinium) Cladocopium goreaui Scavenging Glutathione reductase GR 4 4
Symbiodinium_goreaui-aa-gen (Symbiodinium) Cladocopium goreaui Scavenging Catalase peroxidase Katg 2 2
Symbiodinium_goreaui-aa-gen (Symbiodinium) Cladocopium goreaui Scavenging Monodehydroascorbate reductase MDHAR 1 1
Symbiodinium_goreaui-aa-gen (Symbiodinium) Cladocopium goreaui Scavenging Peroxiredoxin Prxns 12 8
Symbiodinium_goreaui-aa-gen (Symbiodinium) Cladocopium goreaui Scavenging Superoxide Dismutase (Fe, Mn, Cu/Zn, Ni) SOD 4 8
Symbiodinium_kawagutii-aa-gen (Symbiodinium) Fugacium kawagutii Scavenging Ascorbate peroxidase APX 18 16
Symbiodinium_kawagutii-aa-gen (Symbiodinium) Fugacium kawagutii Scavenging Catalase CAT 0 0
Symbiodinium_kawagutii-aa-gen (Symbiodinium) Fugacium kawagutii Scavenging Cytochrome C peroxidase CCP 1 0
Symbiodinium_kawagutii-aa-gen (Symbiodinium) Fugacium kawagutii Scavenging Glutathione peroxidase GPx 1 1
Symbiodinium_kawagutii-aa-gen (Symbiodinium) Fugacium kawagutii Scavenging Glutathione reductase GR 2 1
Symbiodinium_kawagutii-aa-gen (Symbiodinium) Fugacium kawagutii Scavenging Catalase peroxidase Katg 3 1
Symbiodinium_kawagutii-aa-gen (Symbiodinium) Fugacium kawagutii Scavenging Monodehydroascorbate reductase MDHAR 0 0
Symbiodinium_kawagutii-aa-gen (Symbiodinium) Fugacium kawagutii Scavenging Peroxiredoxin Prxns 9 7
Symbiodinium_kawagutii-aa-gen (Symbiodinium) Fugacium kawagutii Scavenging Superoxide Dismutase (Fe, Mn, Cu/Zn, Ni) SOD 7 5
Symbiodinium_minutum-aa-gen (Symbiodinium) Breviolum minutum Scavenging Ascorbate peroxidase APX 16 14
Symbiodinium_minutum-aa-gen (Symbiodinium) Breviolum minutum Scavenging Catalase CAT 0 0
Symbiodinium_minutum-aa-gen (Symbiodinium) Breviolum minutum Scavenging Cytochrome C peroxidase CCP 7 1
Symbiodinium_minutum-aa-gen (Symbiodinium) Breviolum minutum Scavenging Dehydroascorbate reductase DHAR 1 2
Symbiodinium_minutum-aa-gen (Symbiodinium) Breviolum minutum Production Glycolate oxidase GlyOx 2 2
Symbiodinium_minutum-aa-gen (Symbiodinium) Breviolum minutum Scavenging Glutathione peroxidase GPx 1 0
Symbiodinium_minutum-aa-gen (Symbiodinium) Breviolum minutum Scavenging Glutathione reductase GR 5 5
Symbiodinium_minutum-aa-gen (Symbiodinium) Breviolum minutum Scavenging Catalase peroxidase Katg 5 1
Symbiodinium_minutum-aa-gen (Symbiodinium) Breviolum minutum Scavenging Monodehydroascorbate reductase MDHAR 2 2
Symbiodinium_minutum-aa-gen (Symbiodinium) Breviolum minutum Production Nitric oxide synthase NOS 0 2
Symbiodinium_minutum-aa-gen (Symbiodinium) Breviolum minutum Scavenging Peroxiredoxin Prxns 8 8
Symbiodinium_minutum-aa-gen (Symbiodinium) Breviolum minutum Scavenging Superoxide Dismutase (Fe, Mn, Cu/Zn, Ni) SOD 9 5
Symbiodinium_minutum-aa-gen (Symbiodinium) Breviolum minutum Production Sulfite oxidase SulOx 5 5
Symbiodiniun_tridacnidorum-aa-gen Symbiodinium tridacnidorum Scavenging Ascorbate peroxidase APX 16 7
Symbiodiniun_tridacnidorum-aa-gen Symbiodinium tridacnidorum Scavenging Catalase CAT 0 0
Symbiodiniun_tridacnidorum-aa-gen Symbiodinium tridacnidorum Scavenging Cytochrome C peroxidase CCP 2 2
Symbiodiniun_tridacnidorum-aa-gen Symbiodinium tridacnidorum Scavenging Dehydroascorbate reductase DHAR 0 0
Symbiodiniun_tridacnidorum-aa-gen Symbiodinium tridacnidorum Scavenging Glutathione peroxidase GPx 3 2
Symbiodiniun_tridacnidorum-aa-gen Symbiodinium tridacnidorum Scavenging Glutathione reductase GR 4 4
Symbiodiniun_tridacnidorum-aa-gen Symbiodinium tridacnidorum Scavenging Catalase peroxidase Katg 4 1
Symbiodiniun_tridacnidorum-aa-gen Symbiodinium tridacnidorum Scavenging Monodehydroascorbate reductase MDHAR 1 1
Symbiodiniun_tridacnidorum-aa-gen Symbiodinium tridacnidorum Scavenging Peroxiredoxin Prxns 13 4
Symbiodiniun_tridacnidorum-aa-gen Symbiodinium tridacnidorum Scavenging Superoxide Dismutase (Fe, Mn, Cu/Zn, Ni) SOD 11 4
Thalassiosira_antarctica_CCMP982-aa-trans Thalassiosira antarctica Scavenging Catalase CAT 1 1
Thalassiosira_antarctica_CCMP982-aa-trans Thalassiosira antarctica Scavenging Cytochrome C peroxidase CCP 2 4
Thalassiosira_antarctica_CCMP982-aa-trans Thalassiosira antarctica Scavenging Dehydroascorbate reductase DHAR 0 0
Thalassiosira_antarctica_CCMP982-aa-trans Thalassiosira antarctica Production Glycolate oxidase GlyOx 3 2
Thalassiosira_antarctica_CCMP982-aa-trans Thalassiosira antarctica Scavenging Glutathione peroxidase GPx 6 5
Thalassiosira_antarctica_CCMP982-aa-trans Thalassiosira antarctica Scavenging Glutathione reductase GR 6 3
Thalassiosira_antarctica_CCMP982-aa-trans Thalassiosira antarctica Scavenging Catalase peroxidase Katg 3 2
Thalassiosira_antarctica_CCMP982-aa-trans Thalassiosira antarctica Scavenging Monodehydroascorbate reductase MDHAR 0 0
Thalassiosira_antarctica_CCMP982-aa-trans Thalassiosira antarctica Production Nitric oxide synthase NOS 2 5
Thalassiosira_antarctica_CCMP982-aa-trans Thalassiosira antarctica Scavenging Peroxiredoxin Prxns 8 5
Thalassiosira_antarctica_CCMP982-aa-trans Thalassiosira antarctica Scavenging Superoxide Dismutase (Fe, Mn, Cu/Zn, Ni) SOD 7 11
Thalassiosira_antarctica_CCMP982-aa-trans Thalassiosira antarctica Production Sulfite oxidase SulOx 2 2
Thalassiosira_oceanica_CCMP1005-aa-gen Thalassiosira oceanica Scavenging Ascorbate peroxidase APX 14 6
Thalassiosira_oceanica_CCMP1005-aa-gen Thalassiosira oceanica Scavenging Catalase CAT 0 0
Thalassiosira_oceanica_CCMP1005-aa-gen Thalassiosira oceanica Scavenging Cytochrome C peroxidase CCP 2 1
Thalassiosira_oceanica_CCMP1005-aa-gen Thalassiosira oceanica Scavenging Dehydroascorbate reductase DHAR 0 0
Thalassiosira_oceanica_CCMP1005-aa-gen Thalassiosira oceanica Production Glycolate oxidase GlyOx 3 4
Thalassiosira_oceanica_CCMP1005-aa-gen Thalassiosira oceanica Scavenging Glutathione peroxidase GPx 7 6
Thalassiosira_oceanica_CCMP1005-aa-gen Thalassiosira oceanica Scavenging Glutathione reductase GR 4 0
Thalassiosira_oceanica_CCMP1005-aa-gen Thalassiosira oceanica Scavenging Catalase peroxidase Katg 4 4
Thalassiosira_oceanica_CCMP1005-aa-gen Thalassiosira oceanica Scavenging Monodehydroascorbate reductase MDHAR 0 0
Thalassiosira_oceanica_CCMP1005-aa-gen Thalassiosira oceanica Production Nitric oxide synthase NOS 1 3
Thalassiosira_oceanica_CCMP1005-aa-gen Thalassiosira oceanica Scavenging Peroxiredoxin Prxns 7 10
Thalassiosira_oceanica_CCMP1005-aa-gen Thalassiosira oceanica Scavenging Superoxide Dismutase (Fe, Mn, Cu/Zn, Ni) SOD 5 5
Thalassiosira_oceanica_CCMP1005-aa-gen Thalassiosira oceanica Production Sulfite oxidase SulOx 4 3
Thalassiosira_pseudonana_CCMP1335-aa-gen Thalassiosira pseudonana Scavenging Ascorbate peroxidase APX 7 6
Thalassiosira_pseudonana_CCMP1335-aa-gen Thalassiosira pseudonana Scavenging Catalase CAT 0 0
Thalassiosira_pseudonana_CCMP1335-aa-gen Thalassiosira pseudonana Scavenging Cytochrome C peroxidase CCP 0 0
Thalassiosira_pseudonana_CCMP1335-aa-gen Thalassiosira pseudonana Scavenging Dehydroascorbate reductase DHAR 0 0
Thalassiosira_pseudonana_CCMP1335-aa-gen Thalassiosira pseudonana Scavenging Glutathione peroxidase GPx 2 2
Thalassiosira_pseudonana_CCMP1335-aa-gen Thalassiosira pseudonana Scavenging Glutathione reductase GR 3 2
Thalassiosira_pseudonana_CCMP1335-aa-gen Thalassiosira pseudonana Scavenging Catalase peroxidase Katg 1 1
Thalassiosira_pseudonana_CCMP1335-aa-gen Thalassiosira pseudonana Scavenging Monodehydroascorbate reductase MDHAR 0 0
Thalassiosira_pseudonana_CCMP1335-aa-gen Thalassiosira pseudonana Scavenging Peroxiredoxin Prxns 6 8
Thalassiosira_pseudonana_CCMP1335-aa-gen Thalassiosira pseudonana Scavenging Superoxide Dismutase (Fe, Mn, Cu/Zn, Ni) SOD 3 5
Thalassiosira_rotula_CCMP3096-aa-trans Thalassiosira rotula Scavenging Ascorbate peroxidase APX 11 3
Thalassiosira_rotula_CCMP3096-aa-trans Thalassiosira rotula Scavenging Catalase CAT 0 0
Thalassiosira_rotula_CCMP3096-aa-trans Thalassiosira rotula Scavenging Cytochrome C peroxidase CCP 6 5
Thalassiosira_rotula_CCMP3096-aa-trans Thalassiosira rotula Scavenging Dehydroascorbate reductase DHAR 0 0
Thalassiosira_rotula_CCMP3096-aa-trans Thalassiosira rotula Production Glycolate oxidase GlyOx 2 1
Thalassiosira_rotula_CCMP3096-aa-trans Thalassiosira rotula Scavenging Glutathione peroxidase GPx 6 3
Thalassiosira_rotula_CCMP3096-aa-trans Thalassiosira rotula Scavenging Glutathione reductase GR 5 4
Thalassiosira_rotula_CCMP3096-aa-trans Thalassiosira rotula Scavenging Catalase peroxidase Katg 4 1
Thalassiosira_rotula_CCMP3096-aa-trans Thalassiosira rotula Scavenging Monodehydroascorbate reductase MDHAR 0 0
Thalassiosira_rotula_CCMP3096-aa-trans Thalassiosira rotula Production Nitric oxide synthase NOS 2 5
Thalassiosira_rotula_CCMP3096-aa-trans Thalassiosira rotula Scavenging Peroxiredoxin Prxns 8 3
Thalassiosira_rotula_CCMP3096-aa-trans Thalassiosira rotula Scavenging Superoxide Dismutase (Fe, Mn, Cu/Zn, Ni) SOD 3 4
Thalassiosira_rotula_CCMP3096-aa-trans Thalassiosira rotula Production Sulfite oxidase SulOx 3 3

References

1.
Finkel ZV, Beardall J, Flynn KJ, Quigg A, Rees TAV, Raven JA. Phytoplankton in a changing world: Cell size and elemental stoichiometry. J Plankton Res. 2010;32: 119–137. doi:10.1093/plankt/fbp098
2.
Andersen KH, Aksnes DL, Berge T, Fiksen Ø, Visser A. Modelling emergent trophic strategies in plankton. J Plankton Res. 2015;37: 862–868. doi:10.1093/plankt/fbv054
3.
Finkel ZV. Light absorption and size scaling of light-limited metabolism in marine diatoms. Limnol Oceanogr. 2001;46: 86–94. doi:10.4319/lo.2001.46.1.0086
4.
Geider R, Piatt T, Raven J. Size dependence of growth and photosynthesis in diatoms: A synthesis. Mar Ecol Prog Ser. 1986;30: 93–104. doi:10.3354/meps030093
5.
Strom SL, Macri EL, Olson MB. Microzooplankton grazing in the coastal Gulf of Alaska: Variations in top-down control of phytoplankton. Limnol Oceanogr. 2007;52: 1480–1494. doi:10.4319/lo.2007.52.4.1480
6.
Litchman E, de Tezanos Pinto P, Klausmeier CA, Thomas MK, Yoshiyama K. Linking traits to species diversity and community structure in phytoplankton. In: Naselli-Flores L, Rossetti G, editors. Fifty years after the Homage to Santa Rosalia: Old and new paradigms on biodiversity in aquatic ecosystems. Dordrecht: Springer Netherlands; 2010. pp. 15–28. doi:10.1007/978-90-481-9908-2_3
7.
Diaz JM, Plummer S. Production of extracellular reactive oxygen species by phytoplankton: Past and future directions. J Plankton Res. 2018;40: 655–666. doi:10.1093/plankt/fby039
8.
Schneider R. Kinetics of Biological Hydrogen Peroxide Production. Doctor of {{Philosophy}} ({{Geochemistry}}), Colorado School of Mines. 2015.
9.
Lesser MP. Oxidative Stress in Marine Environments: Biochemistry and Physiological Ecology. Annu Rev Physiol. 2006;68: 253–278. doi:10.1146/annurev.physiol.68.040104.110001
10.
Bienert GP, Chaumont F. Aquaporin-facilitated transmembrane diffusion of hydrogen peroxide. Biochim Biophys Acta. 2014;1840: 1596–1604. doi:10.1016/j.bbagen.2013.09.017
11.
Almasalmeh A, Krenc D, Wu B, Beitz E. Structural determinants of the hydrogen peroxide permeability of aquaporins. FEBS J. 2014;281: 647–656. doi:10.1111/febs.12653
12.
Wang H, Schoebel S, Schmitz F, Dong H, Hedfalk K. Characterization of aquaporin-driven hydrogen peroxide transport. Biochim Biophys Acta Biomembr BBA-BIOMEMBRANES. 2020;1862: 183065. doi:10.1016/j.bbamem.2019.183065
13.
Miller EW, Dickinson BC, Chang CJ. Aquaporin-3 mediates hydrogen peroxide uptake to regulate downstream intracellular signaling. PNAS. 2010;107: 15681–15686. doi:10.1073/pnas.1005776107
14.
Zinser ER. The microbial contribution to reactive oxygen species dynamics in marine ecosystems. Environ Microbiol Rep. 2018;10: 412–427. doi:10.1111/1758-2229.12626
15.
Tjell AØ, Almdal K. Diffusion rate of hydrogen peroxide through water-swelled polyurethane membranes. Sensing and Bio-Sensing Research. 2018;21: 35–39. doi:10.1016/j.sbsr.2018.10.001
16.
Adesina AO, Sakugawa H. Photochemically generated nitric oxide in seawater: The peroxynitrite sink and its implications for daytime air quality. Sci Total Environ. 2021;781: 146683. doi:10.1016/j.scitotenv.2021.146683
17.
Korshunov SS, Imlay JA. A potential role for periplasmic superoxide dismutase in blocking the penetration of external superoxide into the cytosol of Gram-negative bacteria. Mol Microbiol. 2002;43: 95–106. doi:10.1046/j.1365-2958.2002.02719.x
18.
Reinsberg PH, Koellisch A, Bawol PP, Baltruschat H. K 2 electrochemistry: Achieving highly reversible peroxide formation. Phys Chem Chem Phys. 2019;21: 4286–4294. doi:10.1039/C8CP06362A
19.
Tian Y, Yang G-P, Liu C-Y, Li P-F, Chen H-T, Bange HW. Photoproduction of nitric oxide in seawater. Ocean Sci. 2020;16: 135–148. doi:10.5194/os-16-135-2020
20.
Olasehinde EF, Takeda K, Sakugawa H. Photochemical Production and Consumption Mechanisms of Nitric Oxide in Seawater. Environ Sci Technol. 2010;44: 8403–8408. doi:10.1021/es101426x
21.
del Río LA, Puppo A, editors. Reactive oxygen species in plant signaling. Dordrecht ; New York: Springer Verlag; 2009.
22.
Zacharia IG, Deen WM. Diffusivity and Solubility of Nitric Oxide in Water and Saline. Ann Biomed Eng. 2005;33: 214–222. doi:10.1007/s10439-005-8980-9
23.
Möller MN, Cuevasanta E, Orrico F, Lopez AC, Thomson L, Denicola A. Diffusion and Transport of Reactive Species Across Cell Membranes. In: Trostchansky A, Rubbo H, editors. Bioactive Lipids in Health and Disease. Cham: Springer International Publishing; 2019. pp. 3–19. doi:10.1007/978-3-030-11488-6_1
24.
Mopper K, Zhou X. Hydroxyl Radical Photoproduction in the Sea and Its Potential Impact on Marine Processes. Science. 1990;250: 661–664. doi:10.1126/science.250.4981.661
25.
Sunday MO, Takeda K, Sakugawa H. Singlet Oxygen Photogeneration in Coastal Seawater: Prospect of Large-Scale Modeling in Seawater Surface and Its Environmental Significance. Environ Sci Technol. 2020;54: 6125–6133. doi:10.1021/acs.est.0c00463
26.
Dill KA, Bromberg S. Molecular driving forces: Statistical thermodynamics in biology, chemistry, physics, and nanoscience. 2nd ed. London ; New York: Garland Science; 2011.
27.
Zielonka J, Sikora A, Joseph J, Kalyanaraman B. Peroxynitrite Is the Major Species Formed from Different Flux Ratios of Co-generated Nitric Oxide and Superoxide: DIRECT REACTION WITH BORONATE-BASED FLUORESCENT PROBE. J Biol Chem. 2010;285: 14210–14216. doi:10.1074/jbc.M110.110080
28.
Marla SS, Lee J, Groves JT. Peroxynitrite rapidly permeates phospholipid membranes. PNAS. 1997;94: 14243–14248. doi:10.1073/pnas.94.26.14243
29.
Hayyan M, Hashim MA, AlNashef IM. Superoxide Ion: Generation and Chemical Implications. Chem Rev. 2016;116: 3029–3085. doi:10.1021/acs.chemrev.5b00407
30.
Fridovich I. Oxygen toxicity: A radical explanation. J Exp Biol. 1998;201: 1203–1209. doi:10.1242/jeb.201.8.1203
31.
Winterbourn CC, Hampton MB. Thiol chemistry and specificity in redox signaling. Free Radic Biol Med. 2008;45: 549–561. doi:10.1016/j.freeradbiomed.2008.05.004
32.
Roe KL, Barbeau KA. Uptake mechanisms for inorganic iron and ferric citrate in Trichodesmium Erythraeum IMS101. Metallomics. 2014;6: 2042–2051. doi:10.1039/c4mt00026a
33.
Rose A. The Influence of Extracellular Superoxide on Iron Redox Chemistry and Bioavailability to Aquatic Microorganisms. Front Microbiol. 2012;3: 124. doi:10.3389/fmicb.2012.00124
34.
Kozuleva MA, Ivanov BN, Vetoshkina DV, Borisova-Mubarakshina MM. Minimizing an Electron Flow to Molecular Oxygen in Photosynthetic Electron Transfer Chain: An Evolutionary View. Front Plant Sci. 2020;11: 211. doi:10.3389/fpls.2020.00211
35.
Blokhina O, Fagerstedt KV. Reactive oxygen species and nitric oxide in plant mitochondria: Origin and redundant regulatory systems. Physiologia Plantarum. 2010;138: 447–462. doi:10.1111/j.1399-3054.2009.01340.x
36.
Asada K, Kiso K, Yoshikawa K. Univalent Reduction of Molecular Oxygen by Spinach Chloroplasts on Illumination. J Biol Chem. 1974;249: 2175–2181. doi:10.1016/S0021-9258(19)42815-9
37.
Kozuleva MA, Ivanov BN. Evaluation of the participation of ferredoxin in oxygen reduction in the photosynthetic electron transport chain of isolated pea thylakoids. Photosynth Res. 2010;105: 51–61. doi:10.1007/s11120-010-9565-5
38.
Pospíšil P. Production of reactive oxygen species by photosystem II. Biochimica et Biophysica Acta (BBA) - Bioenergetics. 2009;1787: 1151–1160. doi:10.1016/j.bbabio.2009.05.005
39.
Pospíšil P. The Role of Metals in Production and Scavenging of Reactive Oxygen Species in Photosystem II. Plant Cell Physiol. 2014;55: 1224–1232. doi:10.1093/pcp/pcu053
40.
Solomon EI, Augustine AJ, Yoon J. O2 Reduction to H2O by the Multicopper Oxidases. Dalton Trans. 2008; 3921–3932. doi:10.1039/b800799c
41.
Messner KR, Imlay JA. Mechanism of Superoxide and Hydrogen Peroxide Formation by Fumarate Reductase, Succinate Dehydrogenase, and Aspartate Oxidase. J Biol Chem. 2002;277: 42563–42571. doi:10.1074/jbc.M204958200
42.
Mittler R. Oxidative stress, antioxidants and stress tolerance. Trends Plant Sci. 2002;7: 405–410. doi:10.1016/S1360-1385(02)02312-9
43.
Zhang T, Hansel CM, Voelker BM, Lamborg CH. Extensive Dark Biological Production of Reactive Oxygen Species in Brackish and Freshwater Ponds. Environ Sci Technol. 2016;50: 2983–2993. doi:10.1021/acs.est.5b03906
44.
Diaz JM, Hansel CM, Voelker BM, Mendes CM, Andeer PF, Zhang T. Widespread Production of Extracellular Superoxide by Heterotrophic Bacteria. Science. 2013;340: 1223–1226. doi:10.1126/science.1237331
45.
Diaz JM, Plummer S, Tomas C, Alves-de-Souza C. Production of extracellular superoxide and hydrogen peroxide by five marine species of harmful bloom-forming algae. J Plankton Res. 2018;40: 667–677. doi:10.1093/plankt/fby043
46.
Rose AL, Webb EA, Waite TD, Moffett JW. Measurement and Implications of Nonphotochemically Generated Superoxide in the Equatorial Pacific Ocean. Environ Sci Technol. 2008;42: 2387–2393. doi:10.1021/es7024609
47.
Schneider RJ, Roe KL, Hansel CM, Voelker BM. Species-Level Variability in Extracellular Production Rates of Reactive Oxygen Species by Diatoms. Frontiers in Chemistry. 2016;4. doi:10.3389/fchem.2016.00005
48.
Sutherland KM, Coe A, Gast RJ, Plummer S, Suffridge CP, Diaz JM, et al. Extracellular superoxide production by key microbes in the global ocean. Limnol Oceanogr. 2019;64: 2679–2693. doi:10.1002/lno.11247
49.
Kustka AB, Shaked Y, Milligan AJ, King DW, Morel FMM. Extracellular production of superoxide by marine diatoms: Contrasting effects on iron redox chemistry and bioavailability. Limnol Oceanogr. 2005;50: 1172–1180. doi:10.4319/lo.2005.50.4.1172
50.
Hansel CM, Buchwald C, Diaz JM, Ossolinski JE, Dyhrman ST, Mooy BASV, et al. Dynamics of extracellular superoxide production by Trichodesmium \(<\backslash\)i\(>\) Colonies from the Sargasso Sea. Limnol Oceanogr. 2016;61: 1188–1200. doi:10.1002/lno.10266
51.
Hansel CM, Diaz JM, Plummer S. Tight Regulation of Extracellular Superoxide Points to Its Vital Role in the Physiology of the Globally Relevant Roseobacter Clade. mBio. 2019;10: e02668–18. doi:10.1128/mBio.02668-18
52.
Marshall J-A, de Salas M, Oda T, Hallegraeff G. Superoxide production by marine microalgae. Mar Biol. 2005;147: 533–540. doi:10.1007/s00227-005-1596-7
53.
Sutherland KM, Grabb KC, Karolewski JS, Plummer S, Farfan GA, Wankel SD, et al. Spatial Heterogeneity in Particle-Associated, Light-Independent Superoxide Production Within Productive Coastal Waters. J Geophys Res Oceans. 2020;125: e2020JC016747. doi:10.1029/2020JC016747
54.
Zafiriou OC. Chemistry of superoxide ion-radical (O2-) in seawater. I. (HOO) and uncatalyzed dismutation kinetics studied by pulse radiolysis. Mar Chem. 1990;30: 31–43. doi:10.1016/0304-4203(90)90060-P
55.
Sutherland KM, Wankel SD, Hansel CM. Dark biological superoxide production as a significant flux and sink of marine dissolved oxygen. PNAS. 2020;117: 3433–3439. doi:10.1073/pnas.1912313117
56.
Wuttig K, Heller MI, Croot PL. Pathways of Superoxide (O 2 ) Decay in the Eastern Tropical North Atlantic. Environ Sci Technol. 2013; 130826150409004. doi:10.1021/es401658t
57.
Scanlan DJ, Ostrowski M, Mazard S, Dufresne A, Garczarek L, Hess WR, et al. Ecological Genomics of Marine Picocyanobacteria. Microbiol Mol Biol Rev. 2009;73: 249–299. doi:10.1128/MMBR.00035-08
58.
Yuan J, Shiller AM. The distribution of hydrogen peroxide in the southern and central Atlantic ocean. Deep Sea Research Part II: Topical Studies in Oceanography. 2001;48: 2947–2970. doi:10.1016/S0967-0645(01)00026-1
59.
Halliwell B, Gutteridge JMC. Free radicals in biology and medicine. 3rd ed. Oxford : New York: Clarendon Press ; Oxford University Press; 1999.
60.
Seaver LC, Imlay JA. Hydrogen peroxide fluxes and compartmentalization inside growing Escherichia Coli. J Bacteriol. 2001;183: 7182–7189. doi:10.1128/JB.183.24.7182-7189.2001
61.
Fedurayev PV, Mironov KS, Gabrielyan DA, Bedbenov VS, Zorina AA, Shumskaya M, et al. Hydrogen Peroxide Participates in Perception and Transduction of Cold Stress Signal in Synechocystis. Plant Cell Physiol. 2018;59: 1255–1264. doi:10.1093/pcp/pcy067
62.
Avery GB, Cooper WJ, Kieber RJ, Willey JD. Hydrogen peroxide at the Bermuda Atlantic Time Series Station: Temporal variability of seawater hydrogen peroxide. Mar Chem. 2005;97: 236–244. doi:10.1016/j.marchem.2005.03.006
63.
Kelly TJ, Daum PH, Schwartz SE. Measurements of peroxides in cloudwater and rain. Journal of Geophysical Research: Atmospheres. 1985;90: 7861–7871. doi:10.1029/JD090iD05p07861
64.
Willey JD, Kieber RJ, Lancaster RD. Coastal rainwater hydrogen peroxide: Concentration and deposition. J Atmos Chem. 1996;25: 149–165. doi:10.1007/BF00053789
65.
Morris JJ, Johnson ZI, Wilhelm SW, Zinser ER. Diel regulation of hydrogen peroxide defenses by open ocean microbial communities. J Plankton Res. 2016;38: 1103–1114. doi:10.1093/plankt/fbw016
66.
Bond RJ, Hansel CM, Voelker BM. Heterotrophic Bacteria Exhibit a Wide Range of Rates of Extracellular Production and Decay of Hydrogen Peroxide. Front Mar Sci. 2020;7: 72. doi:10.3389/fmars.2020.00072
67.
Sengupta D, Mazumder S, Cole JV, Lowry S. Controlling Non-Catalytic Decomposition of High Concentration Hydrogen Peroxide: Fort Belvoir, VA: Defense Technical Information Center; 2004 Feb. doi:10.21236/ADA426795
68.
Millero FJ, Sharma VK, Karn B. The rate of reduction of copper(II) with hydrogen peroxide in seawater. Mar Chem. 1991;36: 71–83. doi:10.1016/S0304-4203(09)90055-X
69.
Moffett JW, Zika RG. Reaction kinetics of hydrogen peroxide with copper and iron in seawater. Environ Sci Technol. 1987;21: 804–810. doi:10.1021/es00162a012
70.
Zinser ER. Cross-protection from hydrogen peroxide by helper microbes: The impacts on the cyanobacterium Prochlorococcus and other beneficiaries in marine communities. Environ Microbiol Rep. 2018;0. doi:10.1111/1758-2229.12625
71.
Morris JJ, Johnson ZI, Szul MJ, Keller M, Zinser ER. Dependence of the Cyanobacterium Prochlorococcus on Hydrogen Peroxide Scavenging Microbes for Growth at the Ocean’s Surface. Rodriguez-Valera F, editor. PLoS ONE. 2011;6: e16805. doi:10.1371/journal.pone.0016805
72.
Morris JJ, Kirkegaard R, Szul MJ, Johnson ZI, Zinser ER. Facilitation of Robust Growth of Prochlorococcus Colonies and Dilute Liquid Cultures by "Helper" Heterotrophic Bacteria. Appl Environ Microbiol. 2008;74: 4530–4534. doi:10.1128/AEM.02479-07
73.
Coe A, Ghizzoni J, LeGault K, Biller S, Roggensack SE, Chisholm SW. Survival of Prochlorococcus in extended darkness. Limnol Oceanogr. 2016;61: 1375–1388. doi:10.1002/lno.10302
74.
Partensky F, Hess WR, Vaulot D. Prochlorococcus, a Marine Photosynthetic Prokaryote of Global Significance. Microbiol Mol Biol Rev. 1999;63: 106–127.
75.
Omar NM, Prášil O, McCain JSP, Campbell DA. Diffusional Interactions among Marine Phytoplankton and Bacterioplankton: Modelling H2O2 as a Case Study. Microorganisms. 2022;10: 821. doi:10.3390/microorganisms10040821
76.
Vardi A, Formiggini F, Casotti R, De Martino A, Ribalet F, Miralto A, et al. A Stress Surveillance System Based on Calcium and Nitric Oxide in Marine Diatoms. PLoS Biol. 2006;4: e60. doi:10.1371/journal.pbio.0040060
77.
Thomson PG. Ecophysiology of the brine dinoflagellate, Polarella Glacialis, and Antarctic fast ice brine communities. PhD thesis, University of Tasmania. 2000.
78.
Zhang Z. Study on patterns and chemical features of NO effect on marine phytoplankton growth. Sci China Ser B. 2005;48: 376. doi:10.1360/03yb0166
79.
Vardi A. Cell signaling in marine diatoms. Commun Integr Biol. 2008;1: 134–136.
80.
Zafiriou OC, McFarland M, Bromund RH. Nitric Oxide in Seawater. Science. 1980;207: 637–639.
81.
Vardi A, Bidle KD, Kwityn C, Hirsh DJ, Thompson SM, Callow JA, et al. A Diatom Gene Regulating Nitric-Oxide Signaling and Susceptibility to Diatom-Derived Aldehydes. Current Biology. 2008;18: 895–899. doi:10.1016/j.cub.2008.05.037
82.
Fujiwara T, Fukumori Y. Cytochrome cb-type nitric oxide reductase with cytochrome c oxidase activity from Paracoccus denitrificans ATCC 35512. J Bacteriol. 1996;178: 1866–1871.
83.
Jahnová J, Luhová L, Petřivalský M. S-Nitrosoglutathione Reductase of Protein S-Nitrosation in Plant NO Signaling. Plants (Basel). 2019;8: 48. doi:10.3390/plants8020048
84.
Collin F. Chemical Basis of Reactive Oxygen Species Reactivity and Involvement in Neurodegenerative Diseases. Int J Mol Sci. 2019;20. doi:10.3390/ijms20102407
85.
Marusawa H, Ichikawa K, Narita N, Murakami H, Ito K, Tezuka T. Hydroxyl radical as a strong electrophilic species. Bioorganic & Medicinal Chemistry. 2002;10: 2283–2290. doi:10.1016/S0968-0896(02)00048-2
86.
Gutteridge JM. Reactivity of hydroxyl and hydroxyl-like radicals discriminated by release of thiobarbituric acid-reactive material from deoxy sugars, nucleosides and benzoate. Biochem J. 1984;224: 761–767.
87.
McGill MR, Jaeschke H. Chapter 4 - Oxidant Stress, Antioxidant Defense, and Liver Injury. In: Kaplowitz N, DeLeve LD, editors. Drug-Induced Liver Disease (Third Edition). Boston: Academic Press; 2013. pp. 71–84. doi:10.1016/B978-0-12-387817-5.00004-2
88.
Davies KJ, Goldberg AL. Proteins damaged by oxygen radicals are rapidly degraded in extracts of red blood cells. J Biol Chem. 1987;262: 8227–8234. doi:10.1016/S0021-9258(18)47553-9
89.
Brezonik PL, Fulkerson-Brekken J. Nitrate-Induced Photolysis in Natural Waters:  Controls on Concentrations of Hydroxyl Radical Photo-Intermediates by Natural Scavenging Agents. Environ Sci Technol. 1998;32: 3004–3010. doi:10.1021/es9802908
90.
Morris JJ, Lenski RE, Zinser ER. The Black Queen Hypothesis: Evolution of Dependencies through Adaptive Gene Loss. mBio. 2012;3: e00036-12-e00036-12. doi:10.1128/mBio.00036-12
91.
Mitchell JG, Seuront L, Doubell MJ, Losic D, Voelcker NH, Seymour J, et al. The Role of Diatom Nanostructures in Biasing Diffusion to Improve Uptake in a Patchy Nutrient Environment. PLoS One. 2013;8: e59548. doi:10.1371/journal.pone.0059548
92.
DellaPenna D, Pogson BJ. VITAMIN SYNTHESIS IN PLANTS: Tocopherols and Carotenoids. Annual Review of Plant Biology. 2006;57: 711–738. doi:10.1146/annurev.arplant.56.032604.144301
93.
Sharma P, Jha AB, Dubey RS, Pessarakli M. Reactive Oxygen Species, Oxidative Damage, and Antioxidative Defense Mechanism in Plants under Stressful Conditions. Journal of Botany. 2012;2012: e217037. doi:10.1155/2012/217037
94.
Database resources of the National Center for Biotechnology Information. Nucleic Acids Res. 2016;44: D7–D19. doi:10.1093/nar/gkv1290
95.
Grigoriev IV, Nordberg H, Shabalov I, Aerts A, Cantor M, Goodstein D, et al. The Genome Portal of the Department of Energy Joint Genome Institute. Nucleic Acids Res. 2012;40: D26–D32. doi:10.1093/nar/gkr947
96.
Nordberg H, Cantor M, Dusheyko S, Hua S, Poliakov A, Shabalov I, et al. The genome portal of the Department of Energy Joint Genome Institute: 2014 updates. Nucleic Acids Res. 2014;42: D26–31. doi:10.1093/nar/gkt1069
97.
Youens-Clark K, Bomhoff M, Ponsero AJ, Wood-Charlson EM, Lynch J, Choi I, et al. iMicrobe: Tools and data-driven discovery platform for the microbiome sciences. GigaScience. 2019;8. doi:10.1093/gigascience/giz083
98.
Leinonen R, Akhtar R, Birney E, Bower L, Cerdeno-Tárraga A, Cheng Y, et al. The European Nucleotide Archive. Nucleic Acids Res. 2011;39: D28–D31. doi:10.1093/nar/gkq967
99.
Vandepoele K, Van Bel M, Richard G, Van Landeghem S, Verhelst B, Moreau H, et al. Pico-PLAZA, a genome database of microbial photosynthetic eukaryotes. Environ Microbiol. 2013;15: 2147–2153. doi:10.1111/1462-2920.12174
100.
Matasci N, Hung L-H, Yan Z, Carpenter EJ, Wickett NJ, Mirarab S, et al. Data access for the 1,000 Plants (1KP) project. Gigascience. 2014;3: 17. doi:10.1186/2047-217X-3-17
101.
Liew YJ, Aranda M, Voolstra CR. Reefgenomics.Org - a repository for marine genomics data. Database (Oxford). 2016;2016. doi:10.1093/database/baw152
102.
Mölder F, Jablonski KP, Letcher B, Hall MB, Tomkins-Tinch CH, Sochat V, et al. Sustainable data analysis with Snakemake. F1000Research; 2021. doi:10.12688/f1000research.29032.2
103.
Huerta-Cepas J, Forslund K, Coelho LP, Szklarczyk D, Jensen LJ, von Mering C, et al. Fast Genome-Wide Functional Annotation through Orthology Assignment by eggNOG-Mapper. Mol Biol Evol. 2017;34: 2115–2122. doi:10.1093/molbev/msx148
104.
Cantalapiedra CP, Hernández-Plaza A, Letunic I, Bork P, Huerta-Cepas J. eggNOG-mapper v2: Functional Annotation, Orthology Assignments, and Domain Prediction at the Metagenomic Scale. Bioinformatics; 2021 Jun. doi:10.1101/2021.06.03.446934
105.
Buchfink B, Xie C, Huson DH. Fast and sensitive protein alignment using DIAMOND. Nat Methods. 2015;12: 59–60. doi:10.1038/nmeth.3176
106.
Huerta-Cepas J, Szklarczyk D, Heller D, Hernández-Plaza A, Forslund SK, Cook H, et al. eggNOG 5.0: A hierarchical, functionally and phylogenetically annotated orthology resource based on 5090 organisms and 2502 viruses. Nucleic Acids Research. 2019;47: D309–D314. doi:10.1093/nar/gky1085
107.
Team RC. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing; 2019.
108.
RStudio Team. RStudio: Integrated Development Environment for R. Boston, MA: RStudio, Inc.; 2015.
109.
Wickham H. Tidyverse: Easily Install and Load the ’Tidyverse’. 2017.
110.
Robinson D, Hayes A. Broom: Convert Statistical Analysis Objects into Tidy Tibbles. 2019.
111.
Bache SM, Wickham H. Magrittr: A Forward-Pipe Operator for R. 2014.
112.
Wickham H, François R, Henry L, Müller K. Dplyr: A Grammar of Data Manipulation. 2018.
113.
Mangiafico S. Rcompanion: Functions to Support Extension Education Program Evaluation. 2020.
114.
Warnes GR, Bolker B, Lumley T, Johnson R. Gmodels: Various R Programming Tools for Model Fitting. 2018.
115.
Kleiber C, Zeileis A. AER: Applied Econometrics with R. 2020.
116.
Warton DI, Duursma RA, Falster DS, Taskinen S. Smatr 3 - an R package for estimation and inference about allometric lines. Methods in Ecology and Evolution. 2012;3: 257–259.
117.
Wickham H. Ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York; 2016.
118.
Wilke CO. Cowplot: Streamlined Plot Theme and Plot Annotations for ’Ggplot2’. 2019.
119.
Hester J. Glue: Interpreted String Literals. 2018.
120.
Zhu H. kableExtra: Construct Complex Table with ’kable’ and Pipe Syntax. 2019.
121.
Wei T, Simko V. R package "corrplot": Visualization of a Correlation Matrix. 2017.
122.
Tang Y, Horikoshi M, Li W. Ggfortify: Unified Interface to Visualize Statistical Result of Popular R Packages. The R Journal. 2016;8: 478–489.
123.
Horikoshi M, Tang Y. Ggfortify: Data Visualization Tools for Statistical Analysis Results. 2018.
124.
Pedersen TL, RStudio. Ggforce: Accelerating ’Ggplot2’. 2021.
125.
Xie Y. Knitr: A Comprehensive Tool for Reproducible Research in R. Implementing Reproducible Research. Chapman and Hall/CRC; 2014.
126.
Xie Y. Dynamic Documents with R and knitr. Second. Boca Raton, Florida: Chapman and Hall/CRC; 2015.
127.
Xie Y. Knitr: A General-Purpose Package for Dynamic Report Generation in R. 2018.
128.
Xie Y. Bookdown: Authoring books and technical documents with R markdown. 2019.
129.
Aust F. Citr: ’RStudioAdd-in to Insert Markdown Citations. 2018.
130.
Chang A, Jeske L, Ulbrich S, Hofmann J, Koblitz J, Schomburg I, et al. BRENDA, the ELIXIR core data resource in 2021: New developments and updates. Nucleic Acids Research. 2021;49: D498–D508. doi:10.1093/nar/gkaa1025
131.
Lundgren CAK, Sjöstrand D, Biner O, Bennett M, Rudling A, Johansson A-L, et al. Scavenging of superoxide by a membrane-bound superoxide oxidase. Nat Chem Biol. 2018;14: 788–793. doi:10.1038/s41589-018-0072-x
132.
Matlashov ME, Belousov VV, Enikolopov G. How Much H2O2 Is Produced by Recombinant D-Amino Acid Oxidase in Mammalian Cells? Antioxid Redox Signal. 2014;20: 1039–1044. doi:10.1089/ars.2013.5618
133.
Bou-Abdallah F, Yang H, Awomolo A, Cooper B, Woodhall MR, Andrews SC, et al. Functionality of the Three-Site Ferroxidase Center of Escherichia Coli Bacterial Ferritin (EcFtnA). Biochemistry. 2014;53: 483–495. doi:10.1021/bi401517f
134.
Fleury K. Reactive Oxygen Detoxification Genes in Phytoplankton. Bachelor of {{Science}}, {{Honors}} Thesis, Mount Allison University. 2019.
135.
Omar N. Reactive Oxygen Production and Scavenging in Marine Phytoplankton. Bachelor of {{Science}}, {{Honors}} Thesis, Mount Allison University. 2020.
136.
Shapiro SS, Wilk MB. An Analysis of Variance Test for Normality (Complete Samples). Biometrika. 1965;52: 591. doi:10.2307/2333709
137.
Anova.glm function - RDocumentation.
138.
Kassambara A. Ggpubr: ’ggplot2’ Based Publication Ready Plots. 2018.
139.
Diaz JM, Plummer S, Hansel CM, Andeer PF, Saito MA, McIlvin MR. NADPH-dependent extracellular superoxide production is vital to photophysiology in the marine diatom Thalassiosira Oceanica\(<\backslash\)i\(>\). PNAS. 2019;116: 16448–16453. doi:10.1073/pnas.1821233116
140.
Mella-Flores D, Six C, Ratin M, Partensky F, Boutte C, Le Corguillé G, et al. Prochlorococcus and Synechococcus have Evolved Different Adaptive Mechanisms to Cope with Light and UV Stress. Front Microbiol. 2012;3: 285. doi:10.3389/fmicb.2012.00285
141.
Pospíšil P. Molecular mechanisms of production and scavenging of reactive oxygen species by photosystem II. Biochimica et Biophysica Acta (BBA) - Bioenergetics. 2012;1817: 218–231. doi:10.1016/j.bbabio.2011.05.017
142.
Pospíšil P. Production of Reactive Oxygen Species by Photosystem II as a Response to Light and Temperature Stress. Frontiers in Plant Science. 2016;7. doi:10.3389/fpls.2016.01950
143.
Bergamini C, Gambetti S, Dondi A, Cervellati C. Oxygen, Reactive Oxygen Species and Tissue Damage. CPD. 2004;10: 1611–1626. doi:10.2174/1381612043384664
144.
Miller A-F. Superoxide dismutases: Ancient enzymes and new insights. FEBS Lett. 2012;586: 585–595. doi:10.1016/j.febslet.2011.10.048
145.
Groussman RD, Parker MS, Armbrust EV. Diversity and Evolutionary History of Iron Metabolism Genes in Diatoms. PLOS ONE. 2015;10: e0129081. doi:10.1371/journal.pone.0129081
146.
Bernroitner M, Zamocky M, Furtmüller PG, Peschek GA, Obinger C. Occurrence, phylogeny, structure, and function of catalases and peroxidases in cyanobacteria. J Exp Bot. 2009;60: 423–440. doi:10.1093/jxb/ern309
147.
Pandey P, Singh J, Achary VMM, Reddy MK. Redox homeostasis via gene families of ascorbate-glutathione pathway. Front Environ Sci. 2015;3. doi:10.3389/fenvs.2015.00025
148.
Randhawa V, Thakkar M, Wei L. Applicability of Hydrogen Peroxide in Brown Tide Control Culture and Microcosm Studies. PLOS ONE. 2012;7: e47844. doi:10.1371/journal.pone.0047844
149.
Picciano AL, Crane BR. A nitric oxide synthaselike protein from Synechococcus produces NO/NO3- from l-arginine and NAPDH in a tetrahydrobiopterin- and Ca2+-dependent manner. J Biol Chem. 2019;294: 10708–10719. doi:10.1074/jbc.RA119.008399
150.
Zweier JL, Samouilov A, Kuppusamy P. Non-enzymatic nitric oxide synthesis in biological systems. Biochim Biophys Acta. 1999;1411: 250–262. doi:10.1016/s0005-2728(99)00018-3
151.
Chen Y-C, Chen Y-H, Chiu H, Ko Y-H, Wang R-T, Wang W-P, et al. Cell-Penetrating Delivery of Nitric Oxide by Biocompatible Dinitrosyl Iron Complex and Its Dermato-Physiological Implications. Int J Mol Sci. 2021;22: 10101. doi:10.3390/ijms221810101
152.
Lampe RH, Wang S, Cassar N, Marchetti A. Strategies among phytoplankton in response to alleviation of nutrient stress in a subtropical gyre. ISME J. 2019;13: 2984–2997. doi:10.1038/s41396-019-0489-6
153.
Peifeng L, Min Z, Chunying L, Guipeng Y. Effects of Nitric Oxide On The Growth of The Marine Microalgae And The Parameters of Carbonate Chemistry. In Review; 2021 May. doi:10.21203/rs.3.rs-521371/v1
154.
Thompson SEM, Taylor AR, Brownlee C, Callow ME, Callow JA. The Role of Nitric Oxide in Diatom Adhesion in Relation to Substratum Properties. J Phycol. 2008;44: 967–976. doi:10.1111/j.1529-8817.2008.00531.x
155.
Hunsucker KZ, Swain GW. In situ measurements of diatom adhesion to silicone-based ship hull coatings. J Appl Phycol. 2016;28: 269–277. doi:10.1007/s10811-015-0584-7
156.
Di Dato V, Musacchia F, Petrosino G, Patil S, Montresor M, Sanges R, et al. Transcriptome sequencing of three Pseudo-nitzschia species reveals comparable gene sets and the presence of Nitric Oxide Synthase genes in diatoms. Scientific Reports. 2015;5: 1–14. doi:10.1038/srep12329
157.
Vihtakari M. ggOceanMaps: Plot Data on Oceanographic Maps using ’Ggplot2’. Zenodo; 2021. doi:10.5281/zenodo.4554715
158.
Re3data.Org. Bigelow National Center for Algae and Microbiota. 2017. doi:10.17616/R3PN76
159.
Hill DRA, Wetherbee R. Guillardia Theta gen. Et sp.nov. (Cryptophyceae). Can J Bot. 1990;68: 1873–1876. doi:10.1139/b90-245
160.
Curtis BA, Tanifuji G, Burki F, Gruber A, Irimia M, Maruyama S, et al. Algal genomes reveal evolutionary mosaicism and the fate of nucleomorphs. Nature. 2012;492: 59–65. doi:10.1038/nature11681
161.
Karlson B, Andreasson A, Johansen M, Karlberg M, Loo A, Skjevik A-T. Nordic Microalgae. http://nordicmicroalgae.org; 2020.
162.
Iwasa K, Shimizu A. Motility of the diatom, Phaeodactylum Tricornutum. Experimental Cell Research. 1972;74: 552–558. doi:10.1016/0014-4827(72)90416-8
163.
Bowler C, Allen AE, Badger JH, Grimwood J, Jabbari K, Kuo A, et al. The Phaeodactylum genome reveals the evolutionary history of diatom genomes. Nature. 2008;456: 239–244. doi:10.1038/nature07410
164.
Ova Ozcan D, Ovez B. Evaluation of the interaction of temperature and light intensity on the growth of Phaeodactylum Tricornutum: Kinetic modeling and optimization. Biochemical Engineering Journal. 2020;154: 107456. doi:10.1016/j.bej.2019.107456
165.
Matsumoto M, Mayama S, Nemoto M, Fukuda Y, Muto M, Yoshino T, et al. Morphological and molecular phylogenetic analysis of the high triglyceride-producing marine diatom, Fistulifera Solaris Sp. Nov. (Bacillariophyceae). Phycological Research. 2014;62: 257–268. doi:10.1111/pre.12066
166.
Nanjappa D, Sanges R, Ferrante MI, Zingone A. Diatom flagellar genes and their expression during sexual reproduction in Leptocylindrus Danicus. BMC Genomics. 2017;18: 813. doi:10.1186/s12864-017-4210-8
167.
Tanaka T, Maeda Y, Veluchamy A, Tanaka M, Abida H, Maréchal E, et al. Oil accumulation by the oleaginous diatom Fistulifera Solaris as revealed by the genome and transcriptome. Plant Cell. 2015;27: 162–176. doi:10.1105/tpc.114.135194
168.
Misumi O, Yoshida Y, Nishida K, Fujiwara T, Sakajiri T, Hirooka S, et al. Genome analysis and its significance in four unicellular algae, Cyanidioshyzon Merolae, Ostreococcus Tauri, Chlamydomonas Reinhardtii, and Thalassiosira Pseudonana. Journal of Plant Research. 2008;121: 3–17. doi:10.1007/s10265-007-0133-9
169.
Armbrust EV, Berges JA, Bowler C, Green BR, Martinez D, Putnam NH, et al. The Genome of the Diatom Thalassiosira Pseudonana: Ecology, Evolution, and Metabolism. Science. 2004;306: 79–86. doi:10.1126/science.1101156
170.
Hevia-Orube J, Orive E, David H, Díez A, Laza-Martínez A, Miguel I, et al. Molecular and morphological analyses of solitary forms of brackish Thalassiosiroid diatoms (Coscinodiscophyceae), with emphasis on their phenotypic plasticity. European Journal of Phycology. 2016;51: 11–30. doi:10.1080/09670262.2015.1077394
171.
Lommer M, Roy A-S, Schilhabel M, Schreiber S, Rosenstiel P, LaRoche J. Recent Transfer of an Iron-Regulated Gene from the Plastid to the Nuclear Genome in an Oceanic Diatom Adapted to Chronic Iron Limitation. BMC Genomics. 2010;11: 718. doi:10.1186/1471-2164-11-718
172.
Bucciarelli E, Pondaven P, Sarthou G. Effects of an iron-light co-limitation on the elemental composition (Si, C, N) of the marine diatoms &lt;i&gt;Thalassiosira oceanica&lt;/i&gt; and &lt;i&gt;Ditylum brightwellii&lt;/i&gt; Biogeochemistry: Open Ocean; 2009 Jul. doi:10.5194/bgd-6-7175-2009
173.
Johnson LK, Alexander H, Brown CT. Re-assembly, quality evaluation, and annotation of 678 microbial eukaryotic reference transcriptomes. GigaScience. 2019;8. doi:10.1093/gigascience/giy158
174.
Miklasz KA. Physical Constraints on the Size and Shape of Microalgae. PhD thesis, Stanford University. 2012.
175.
Phyto’pedia. Phyto’pedia - The Phytoplankton Encyclopedia Project. https://www.eoas.ubc.ca/research/phytoplankton/index.html; 2012.
176.
Keeling PJ, Burki F, Wilcox HM, Allam B, Allen EE, Amaral-Zettler LA, et al. The Marine Microbial Eukaryote Transcriptome Sequencing Project (MMETSP): Illuminating the Functional Diversity of Eukaryotic Life in the Oceans through Transcriptome Sequencing. PLOS Biology. 2014;12: e1001889. doi:10.1371/journal.pbio.1001889
177.
Gaonkar CC, Piredda R, Sarno D, Zingone A, Montresor M, Kooistra WHCF. Species detection and delineation in the marine planktonic diatoms Chaetoceros and Bacteriastrum through metabarcoding: Making biological sense of haplotype diversity. Environmental Microbiology. 2020;22: 1917–1929. doi:10.1111/1462-2920.14984
178.
Traller JC, Cokus SJ, Lopez DA, Gaidarenko O, Smith SR, McCrow JP, et al. Genome and methylome of the oleaginous diatom Cyclotella Cryptica reveal genetic flexibility toward a high lipid phenotype. Biotechnology for Biofuels. 2016;9: 258. doi:10.1186/s13068-016-0670-3
179.
Guo L, Liang S, Zhang Z, Liu H, Wang S, Pan K, et al. Genome assembly of Nannochloropsis oceanica provides evidence of host nucleus overthrow by the symbiont nucleus during speciation. Communications Biology. 2019;2: 1–12. doi:10.1038/s42003-019-0500-9
180.
Vieler A, Wu G, Tsai C-H, Bullard B, Cornish AJ, Harvey C, et al. Genome, Functional Gene Annotation, and Nuclear Transformation of the Heterokont Oleaginous Alga Nannochloropsis oceanica CCMP1779. PLOS Genetics. 2012;8: e1003064. doi:10.1371/journal.pgen.1003064
181.
Suda S, Atsumi M, Miyashita H. Taxonomic characterization of a marine Nannochloropsis species, N. Oceanica Sp. Nov. (Eustigmatophyceae). Phycologia. 2002;41: 273–279. doi:10.2216/i0031-8884-41-3-273.1
182.
Radakovits R, Jinkerson RE, Fuerstenberg SI, Tae H, Settlage RE, Boore JL, et al. Draft genome sequence and genetic transformation of the oleaginous alga Nannochloropis Gaditana. Nat Commun. 2012;3: 686. doi:10.1038/ncomms1688
183.
Mitra M, Patidar SK, George B, Shah F, Mishra S. A euryhaline Nannochloropsis Gaditana with potential for nutraceutical (EPA) and biodiesel production. Algal Research. 2015;8: 161–167. doi:10.1016/j.algal.2015.02.006
184.
Ohan JA, Hovde BT, Zhang XL, Davenport KW, Chertkov O, Han C, et al. Nuclear Genome Assembly of the Microalga Nannochloropsis Salina CCMP1776. Microbiology Resource Announcements. 2019;8: e00750–19. doi:10.1128/MRA.00750-19
185.
Oliver A, Podell S, Pinowska A, Traller JC, Smith SR, McClure R, et al. Diploid genomic architecture of Nitzschia Inconspicua, an elite biomass production diatom. Sci Rep. 2021;11: 15592. doi:10.1038/s41598-021-95106-3
186.
Krienitz L, Krienitz D, Dadheech PK, Hübener T, Kotut K, Luo W, et al. Food algae for Lesser Flamingos: A stocktaking. Hydrobiologia. 2016;775: 21–50. doi:10.1007/s10750-016-2706-x
187.
Vaulot D, Gall F, Le Marie D, Guillou L, Partensky F. The Roscoff Culture Collection (RCC): A collection dedicated to marine picoplankton. nova_hedwigia. 2004;79: 49–70. doi:10.1127/0029-5035/2004/0079-0049
188.
Palenik B, Grimwood J, Aerts A, Rouzé P, Salamov A, Putnam N, et al. The tiny eukaryote Ostreococcus provides genomic insights into the paradox of plankton speciation. PNAS. 2007;104: 7705–7710. doi:10.1073/pnas.0611046104
189.
Blanc-Mathieu R, Krasovec M, Hebrard M, Yau S, Desgranges E, Martin J, et al. Population genomics of picophytoplankton unveils novel chromosome hypervariability. Sci Adv. 2017;3: e1700239. doi:10.1126/sciadv.1700239
190.
Ooijen G van, Knox K, Kis K, Bouget F-Y, Millar AJ. Genomic Transformation of the Picoeukaryote Ostreococcus tauri. JoVE (Journal of Visualized Experiments). 2012; e4074. doi:10.3791/4074
191.
Moreau H, Verhelst B, Couloux A, Derelle E, Rombauts S, Grimsley N, et al. Gene functionalities and genome structure in Bathycoccus Prasinos reflect cellular specializations at the base of the green lineage. Genome Biology. 2012;13: R74. doi:10.1186/gb-2012-13-8-r74
192.
Marin B, Melkonian M. Molecular Phylogeny and Classification of the Mamiellophyceae class. Nov. (Chlorophyta) based on Sequence Comparisons of the Nuclear- and Plastid-encoded rRNA Operons. Protist. 2010;161: 304–336. doi:10.1016/j.protis.2009.10.002
193.
Throndsen J. The Planktonic Marine Flagellates. Identifying Marine Phytoplankton. Elsevier; 1997. pp. 591–729. doi:10.1016/B978-012693018-4/50007-0
194.
Worden AZ, Lee J-H, Mock T, Rouzé P, Simmons MP, Aerts AL, et al. Green evolution and dynamic adaptations revealed by genomes of the marine picoeukaryotes Micromonas. Science. 2009;324: 268–272. doi:10.1126/science.1167222
195.
Šlapeta J, López-García P, Moreira D. Global Dispersal and Ancient Cryptic Species in the Smallest Marine Eukaryotes. Mol Biol Evol. 2006;23: 23–29. doi:10.1093/molbev/msj001
196.
Blanc G, Agarkova I, Grimwood J, Kuo A, Brueggeman A, Dunigan DD, et al. The genome of the polar eukaryotic microalga Coccomyxa Subellipsoidea reveals traits of cold adaptation. Genome Biol. 2012;13: R39. doi:10.1186/gb-2012-13-5-r39
197.
Takahashi K, Ide Y, Hayakawa J, Yoshimitsu Y, Fukuhara I, Abe J, et al. Lipid productivity in TALEN-induced starchless mutants of the unicellular green alga Coccomyxa Sp. Strain Obi. Algal Research. 2018;32: 300–307. doi:10.1016/j.algal.2018.04.020
198.
Heimann K, Huerlimann R. Chapter 3 - Microalgal Classification: Major Classes and Genera of Commercial Microalgal Species. In: Kim S-K, editor. Handbook of Marine Microalgae. Boston: Academic Press; 2015. pp. 25–41. doi:10.1016/B978-0-12-800776-1.00003-0
199.
Oren A. A hundred years of Dunaliella research: 1905. Saline Systems. 2005;1: 2. doi:10.1186/1746-1448-1-2
200.
Polle JEW, Barry K, Cushman J, Schmutz J, Tran D, Hathwaik LT, et al. Draft Nuclear Genome Sequence of the Halophilic and Beta-Carotene-Accumulating Green Alga Dunaliella Salina Strain CCAP19/18. Genome Announc. 2017;5. doi:10.1128/genomeA.01105-17
201.
dos Santos AL, Pollina T, Gourvil P, Corre E, Marie D, Garrido JL, et al. Chloropicophyceae, a New Class of Picophytoplanktonic Prasinophytes. Scientific Reports. 2017;7: 1–20. doi:10.1038/s41598-017-12412-5
202.
Krienitz L, Bock C, Kotut K, Luo W. Picocystis Salinarum (Chlorophyta) in saline lakes and hot springs of East Africa. Phycologia. 2012;51: 22–32. doi:10.2216/11-28.1
203.
Borowitzka MA. Biology of Microalgae. Microalgae in Health and Disease Prevention. Elsevier; 2018. pp. 23–72. doi:10.1016/B978-0-12-811405-6.00003-7
204.
Read BA, Kegel J, Klute MJ, Kuo A, Lefebvre SC, Maumus F, et al. Pan genome of the phytoplankton Emiliania underpins its global distribution. Nature. 2013;499: 209–213. doi:10.1038/nature12221
205.
Sekino K, Kobayashi H, Shiraiwa Y. Role of Coccoliths in the Utilization of Inorganic Carbon by a Marine Unicellular Coccolithophorid, Emiliania Huxleyi: A Survey Using Intact Cells and Protoplasts. Plant Cell Physiol. 1996;37: 123–127. doi:10.1093/oxfordjournals.pcp.a028921
206.
Bendif EM, Probert I, Schroeder DC, de Vargas C. On the Description of Tisochrysis Lutea Gen. Nov. Sp. Nov. And Isochrysis Nuda Sp. Nov. In the Isochrysidales, and the Transfer of Dicrateria to the Prymnesiales (Haptophyta). J Appl Phycol. 2013;25: 1763–1776. doi:10.1007/s10811-013-0037-0
207.
Carrier G, Baroukh C, Rouxel C, Duboscq-Bidot L, Schreiber N, Bougaran G. Draft genomes of the algae Tisochrysis lutea strains. SEANOE; 2017. doi:10.17882/47171
208.
Beltrami E. Chapter 9 - Viral Outbreaks and Blood Clots. In: Beltrami E, editor. Mathematical Models for Society and Biology (Second Edition). Boston: Academic Press; 2013. pp. 159–186. doi:10.1016/B978-0-12-404624-5.00009-1
209.
Caron DA, Dennett MR, Moran DM, Schaffner RA, Lonsdale DJ, Gobler CJ, et al. Development and Application of a Monoclonal-Antibody Technique for Counting Aureococcus anophagefferens, an Alga Causing Recurrent Brown Tides in the Mid-Atlantic United States. Appl Environ Microbiol. 2003;69: 5492–5502. doi:10.1128/AEM.69.9.5492-5502.2003
210.
Gobler CJ, Berry DL, Dyhrman ST, Wilhelm SW, Salamov A, Lobanov AV, et al. Niche of harmful alga Aureococcus anophagefferens revealed through ecogenomics. Proc Natl Acad Sci U S A. 2011;108: 4352–4357. doi:10.1073/pnas.1016106108
211.
Sieburth JMcN, Johnson PW, Hargraves PE. Ultrastructure and Ecology of Aureococcus Anophageferens Gen. Et Sp. Nov. (Chrysophyceae): The Dominant Picoplankter During a Bloom in Narragansett Bay, Rhode Island, Summer 19851. J Phycol. 1988;24: 416–425. doi:10.1111/j.1529-8817.1988.tb04485.x
212.
Ribeiro CG, Santos AL dos, Gourvil P, Gall FL, Marie D, Tragin M, et al. Culturable diversity of Arctic phytoplankton during pack ice melting. bioRxiv. 2019; 642264. doi:10.1101/642264
213.
Roy S, editor. Phytoplankton pigments: Characterization, chemotaxonomy, and applications in oceanography. Cambridge ; New York: Cambridge University Press; 2011.
214.
Bhattacharya D, Price DC, Chan CX, Qiu H, Rose N, Ball S, et al. Genome of the red alga Porphyridium Purpureum. Nat Commun. 2013;4: 1941. doi:10.1038/ncomms2931
215.
Markina ZV, Orlova TYu, Vasyanovich YA, Vardavas AI, Stivaktakis PD, Vardavas CI, et al. Porphyridium purpureum microalga physiological and ultrastructural changes under copper intoxication. Toxicology Reports. 2021;8: 988–993. doi:10.1016/j.toxrep.2021.04.015
216.
Aguilo-Ferretjans M del M, Bosch R, Puxty RJ, Latva M, Zadjelovic V, Chhun A, et al. Pili allow dominant marine cyanobacteria to avoid sinking and evade predation. Nat Commun. 2021;12: 1857. doi:10.1038/s41467-021-22152-w
217.
Kettler GC, Martiny AC, Huang K, Zucker J, Coleman ML, Rodrigue S, et al. Patterns and implications of gene gain and loss in the evolution of Prochlorococcus. PLoS Genet. 2007;3: e231. doi:10.1371/journal.pgen.0030231
218.
Park JS, Han J, Suh S-S, Kim H-J, Lee T-K, Jung SW. Characterization of bacterial community structure in two alcyonacean soft corals (Litophyton Sp. And Sinularia Sp.) From Chuuk, Micronesia. Coral Reefs. 2021. doi:10.1007/s00338-021-02176-w
219.
Dufresne A, Salanoubat M, Partensky F, Artiguenave F, Axmann IM, Barbe V, et al. Genome sequence of the cyanobacterium Prochlorococcus marinus SS120, a nearly minimal oxyphototrophic genome. Proc Natl Acad Sci USA. 2003;100: 10020–10025. doi:10.1073/pnas.1733211100
220.
Rocap G, Larimer FW, Lamerdin J, Malfatti S, Chain P, Ahlgren NA, et al. Genome divergence in two Prochlorococcus ecotypes reflects oceanic niche differentiation. Nature. 2003;424: 1042–1047. doi:10.1038/nature01947
221.
Gu H, Zeng N, Xie Z, Wang D, Wang W, Yang W. Morphology, phylogeny, and toxicity of Atama complex (Dinophyceae) from the Chukchi Sea. Polar Biol. 2013;36: 427–436. doi:10.1007/s00300-012-1273-5
222.
Lim AS, Jeong HJ, Ok JH, Kim SJ. Feeding by the harmful phototrophic dinoflagellate Takayama Tasmanica (Family Kareniaceae). Harmful Algae. 2018;74: 19–29. doi:10.1016/j.hal.2018.03.009
223.
Sharma VK, Rhudy KB, Millero FJ. Diurnal variation of texas “brown tide” (Aureoumbra Lagunensis) in relation to metals. Journal of Environmental Science and Health, Part A. 2000;35: 1077–1088. doi:10.1080/10934520009377021
224.
Harvey EL, Menden-Deuer S, Rynearson TA. Persistent Intra-Specific Variation in Genetic and Behavioral Traits in the Raphidophyte, Heterosigma akashiwo. Front Microbiol. 2015;6.
225.
Dursun F, Taş S, Koray T. Spring bloom of the raphidophycean Heterosigma akashiwo in the Golden Horn Estuary at the northeast of Sea of Marmara. EgeJFAS. 2016;33: 201. doi:10.12714/egejfas.2016.33.3.03
226.
Wang C, Lan CQ. Effects of shear stress on microalgae A review. Biotechnology Advances. 2018;36: 986–1002. doi:10.1016/j.biotechadv.2018.03.001
227.
Iba W. Isolation and Growth of Dinoglagellate, Scrippsiella Sp. And diatom, Melosira Cf. Moniliformis in controlled conditions. Indones Aquac J. 2014;9: 55–63. doi:10.15578/iaj.9.1.2014.55-63
228.
Kawachi M. Microbial Culture Collection, National Institute for Environmental Studies. National Institute of Genetics, ROIS; 2021. doi:10.15468/8RML10
229.
Yamaguchi H, Shimura Y, Suzuki S, Yamagishi T, Tatarazako N, Kawachi M. Complete Genome Sequence of Cyanobium Sp. NIES-981, a Marine Strain Potentially Useful for Ecotoxicological Bioassays. Genome Announc. 2016;4: e00736–16. doi:10.1128/genomeA.00736-16
230.
Albrecht M, Pröschold T, Schumann R. Identification of Cyanobacteria in a Eutrophic Coastal Lagoon on the Southern Baltic Coast. Front Microbiol. 2017;8: 923. doi:10.3389/fmicb.2017.00923
231.
Hirakawa Y, Howe A, James ER, Keeling PJ. Morphological Diversity between Culture Strains of a Chlorarachniophyte, Lotharella Globosa. PLOS ONE. 2011;6: e23193. doi:10.1371/journal.pone.0023193
232.
Ota S, Vaulot D, Gall FL, Yabuki A, Ishida K. Partenskyella Glossopodia gen. Et sp. Nov., The First Report of a Chlorarachniophyte that Lacks a Pyrenoid. Protist. 2009;160: 137–150. doi:10.1016/j.protis.2008.09.003
233.
Biller SJ, Berube PM, Berta-Thompson JW, Kelly L, Roggensack SE, Awad L, et al. Genomes of diverse isolates of the marine cyanobacterium Prochlorococcus. Sci Data. 2014;1: 140034. doi:10.1038/sdata.2014.34
234.
Shimada A, Nishijima M, Maruyama T. Seasonal appearance of Prochlorococcus in Suruga Bay, Japan in 1992. J Oceanogr. 1995;51: 289–300. doi:10.1007/BF02285167
235.
Urbach E, Scanlan DJ, Distel DL, Waterbury JB, Chisholm SW. Rapid Diversification of Marine Picophytoplankton with Dissimilar Light-Harvesting Structures Inferred from Sequences of Prochlorococcus and Synechococcus (Cyanobacteria). J Mol Evol. 1998;46: 188–201. doi:10.1007/PL00006294
236.
Rocap G, Distel DL, Waterbury JB, Chisholm SW. Resolution of Prochlorococcus and Synechococcus Ecotypes by Using 16S-23S Ribosomal DNA Internal Transcribed Spacer Sequences. Appl Environ Microbiol. 2002;68: 1180–1191. doi:10.1128/AEM.68.3.1180-1191.2002
237.
Brahamsha B. An abundant cell-surface polypeptide is required for swimming by the nonflagellated marine cyanobacterium Synechococcus. Proc Natl Acad Sci U S A. 1996;93: 6504–6509.
238.
Rosales N, Ortega J, Mora R, Morales E. Influence of salinity on the growth and biochemical composition of the cyanobacterium Synechococcus Sp. Ciencias Marinas. 2005;31: 349–355. doi:10.7773/cm.v31i2.59
239.
Wei Y, Sun J, Zhang X, Wang J, Huang K. Picophytoplankton size and biomass around equatorial eastern Indian Ocean. Microbiologyopen. 2018;8: e00629. doi:10.1002/mbo3.629
240.
Marston MF, Polson SW. Whole-Genome Sequence of the Cyanobacterium Synechococcus sp. Strain WH 8101. Stewart FJ, editor. Microbiol Resour Announc. 2020;9. doi:10.1128/MRA.01593-19
241.
Shimura Y, Hirose Y, Misawa N, Wakazuki S, Fujisawa T, Nakamura Y, et al. Complete Genome Sequence of a Coastal Cyanobacterium, Synechococcus Sp. Strain NIES-970. Genome Announc. 2017;5: e00139–17. doi:10.1128/genomeA.00139-17
242.
Ramos V, Morais J, Castelo-Branco R, Pinheiro Â, Martins J, Regueiras A, et al. Cyanobacterial diversity held in microbial biological resource centers as a biotechnological asset: The case study of the newly established LEGE culture collection. J Appl Phycol. 2018;30: 1437–1451. doi:10.1007/s10811-017-1369-y
243.
Wilde A, Mullineaux CW. Motility in cyanobacteria: Polysaccharide tracks and Type IV pilus motors. Molecular Microbiology. 2015;98: 998–1001. doi:10.1111/mmi.13242
244.
Vogt RA, Ignoffo TR, Sullivan LJ, Herndon J, Stillman JH, Kimmerer WJ. Feeding capabilities and limitations in the nauplii of two pelagic estuarine copepods, Pseudodiaptomus Marinus and Oithona Davisae. Limnol Oceanogr. 2013;58: 2145–2157. doi:10.4319/lo.2013.58.6.2145
245.
Mock T, Otillar RP, Strauss J, McMullan M, Paajanen P, Schmutz J, et al. Evolutionary genomics of the cold-adapted diatom Fragilariopsis Cylindrus. Nature. 2017;541: 536–540. doi:10.1038/nature20803
246.
Findlay CR, Wiens R, Rak M, Sedlmair J, J. Hirschmugl C, Morrison J, et al. Rapid biodiagnostic ex vivo imaging at 1 \(M\)m pixel resolution with thermal source FTIR FPA. Analyst. 2015;140: 2493–2503. doi:10.1039/C4AN01982B
247.
D’Alelio D, Amato A, Luedeking A, Montresor M. Sexual and vegetative phases in the planktonic diatom Pseudo-nitzschia multistriata. Harmful Algae. 2009;8: 225–232. doi:10.1016/j.hal.2008.05.004
248.
Ferrante IM. Pseudo-nitzschia multistriata strain B856, whole genome shotgun sequencing project. NCBI Direct Sumbission. 2019.
249.
Orlova TYu, Stonik IV, Aizdaicher NA, Bates SS, Léger C, Fehling J. Toxicity, morphology and distribution of Pseudo-nitzschia Calliantha, P. Multistriata and P. Multiseries (Bacillariophyta) from the northwestern Sea of Japan. Botanica Marina. 2008;51. doi:10.1515/BOT.2008.035
250.
Davidovich NA, Bates SS. Patterns of Sexual Reproduction in the Pennate Diatoms Pseudo-nitzschia multiseries AND P. pseudodelicatissima. 1998; 4.
251.
Lundholm N, Daugbjerg N, Moestrup Ø. Phylogeny of the Bacillariaceae with emphasis on the genus Pseudo - Nitzschia (Bacillariophyceae) based on partial LSU rDNA. European Journal of Phycology. 2002;37: 115–134. doi:10.1017/S096702620100347X
252.
Kociolek P. Nitzschia palea. In Diatoms of North America. https://diatoms.org/species/nitzschia_palea; 2011.
253.
Garacci M, Barret M, Folgoas C, Flahaut E, Chimowa G, Bertucci A, et al. Transcriptomic response of the benthic freshwater diatom Nitzschia Palea exposed to Few Layer Graphene. Environmental Science: Nano. 2019;6: 1363–1381. doi:10.1039/C8EN00987B
254.
Crowell RM, Nienow JA, Cahoon AB. The complete chloroplast and mitochondrial genomes of the diatom Nitzschia Palea \(<\backslash\)i\(>\) (Bacillariophyceae) Demonstrate High Sequence Similarity to the Endosymbiont Organelles of the Dinotom Durinskia Baltica \(<\backslash\)i\(>\). J Phycol. 2019;55: 352–364. doi:10.1111/jpy.12824
255.
Kessenich CR, Ruck EC, Schurko AM, Wickett NJ, Alverson AJ. Transcriptomic Insights into the Life History of Bolidophytes, the Sister Lineage to Diatoms. J Phycol. 2014;50: 977–983. doi:10.1111/jpy.12222
256.
Mahadik GA, Castellani C, Mazzocchi MG. Effect of diatom morphology on the small-scale behavior of the copepod Temora stylifera (Dana, 1849). J Exp Mar Biol Ecol. 2017;493: 41–48. doi:10.1016/j.jembe.2017.05.001
257.
Gérikas Ribeiro C, dos Santos AL, Gourvil P, Le Gall F, Marie D, Tragin M, et al. Culturable diversity of Arctic phytoplankton during pack ice melting. Deming JW, Michel C, editors. Elementa: Science of the Anthropocene. 2020;8. doi:10.1525/elementa.401
258.
Balzano S, Percopo I, Siano R, Gourvil P, Chanoine M, Marie D, et al. Morphological and genetic diversity of Beaufort Sea diatoms with high contributions from the Chaetoceros Neogracilis species complex. J Phycol. 2017;53: 161–187. doi:10.1111/jpy.12489
259.
Fernandes LF, Frassão-Santos EK. Mucilaginous species of Thalassiosira \(<\backslash\)i\(>\) Cleve Emend: Hasle (Diatomeae) in South Brazilian Waters. Acta Bot Bras. 2011;25: 31–42. doi:10.1590/S0102-33062011000100006
260.
McFarland M, Nayak AR, Stockley N, Twardowski M, Sullivan J. Enhanced Light Absorption by Horizontally Oriented Diatom Colonies. Front Mar Sci. 2020;7: 494. doi:10.3389/fmars.2020.00494
261.
Finenko ZZ, Krupatkina-Akinina DK. Effect of inorganic phosphorus on the growth rate of diatoms. Mar Biol. 1974;26: 193–201. doi:10.1007/BF00389251
262.
Lajeunesse TC, Parkinson JE, Reimer JD. A Genetics-Based Description of Symbiodinium Minutum Sp. Nov. And S. Psygmophilum Sp. Nov. (Dinophyceae), Two Dinoflagellates Symbiotic with Cnidaria. J Phycol. 2012;48: 1380–1391. doi:10.1111/j.1529-8817.2012.01217.x
263.
Shinzato C, Mungpakdee S, Satoh N, Shoguchi E. A genomic approach to coral-dinoflagellate symbiosis: Studies of Acropora Digitifera and Symbiodinium Minutum. Front Microbiol. 2014;5. doi:10.3389/fmicb.2014.00336
264.
Shoguchi E, Shinzato C, Kawashima T, Gyoja F, Mungpakdee S, Koyanagi R, et al. Draft Assembly of the Symbiodinium Minutum Nuclear Genome Reveals Dinoflagellate Gene Structure. Current Biology. 2013;23: 1399–1408. doi:10.1016/j.cub.2013.05.062
265.
Kirk AL, Clowez S, Lin F, Grossman AR, Xiang T. Transcriptome Reprogramming of Symbiodiniaceae Breviolum Minutum in Response to Casein Amino Acids Supplementation. Front Physiol. 2020;11: 574654. doi:10.3389/fphys.2020.574654
266.
Chen W-NU, Hsiao Y-J, Mayfield AB, Young R, Hsu L-L, Peng S-E. Transmission of a heterologous clade C Symbiodinium in a model anemone infection system via asexual reproduction. PeerJ. 2016;4. doi:10.7717/peerj.2358
267.
Martinez S, Kolodny Y, Shemesh E, Scucchia F, Nevo R, Levin-Zaidman S, et al. Energy Sources of the Depth-Generalist Mixotrophic Coral StylophoraPistillata . Front Mar Sci. 2020;7: 988. doi:10.3389/fmars.2020.566663
268.
Montresor M, Lovejoy C, Orsini L, Procaccini G, Roy S. Bipolar distribution of the cyst-forming dinoflagellate Polarella Glacialis. Polar Biol. 2003;26: 186–194. doi:10.1007/s00300-002-0473-9
269.
Stephens TG, González-Pech RA, Cheng Y, Mohamed AR, Bhattacharya D, Ragan MA, et al. Polarella Glacialis genomes encode tandem repeats of single-exon genes with functions critical to adaptation of dinoflagellates. bioRxiv. 2019; 704437. doi:10.1101/704437
270.
Thomson PG, Wright SW, Bolch CJS, Nichols PD, Skerratt JH, McMinn A. Antarctic Distribution, Pigment and Lipid Composition, and Molecular Identification of the Brine Dinoflagellate Polarella Glacialis (dinophyceae)1. J Phycol. 2004;40: 867–873. doi:10.1111/j.1529-8817.2004.03169.x
271.
Wang J, Zhu J, Liu S, Liu B, Gao Y, Wu Z. Generation of reactive oxygen species in cyanobacteria and green algae induced by allelochemicals of submerged macrophytes. Chemosphere. 2011;85: 977–982. doi:10.1016/j.chemosphere.2011.06.076
272.
Zingone A, Forlani G, Percopo I, Montresor M. Morphological characterization of Phaeocystis Antarctica (Prymnesiophyceae). Phycologia. 2011;50: 650–660. doi:10.2216/11-36.1
273.
Shields AR, Smith WO. Size-fractionated photosynthesis/irradiance relationships during Phaeocystis antarctica-dominated blooms in the Ross Sea, Antarctica. J Plankton Res. 2009;31: 701–712. doi:10.1093/plankt/fbp022
274.
Peperzak L, Colijn F, Vrieling EG, Gieskes WWC, Peeters JCH. Observations of flagellates in colonies of Phaeocystis Globosa (Prymnesiophyceae); a hypothesis for their position in the life cycle. J Plankton Res. 2000;22: 2181–2203. doi:10.1093/plankt/22.12.2181
275.
Smayda TJ. Turbulence, watermass stratification and harmful algal blooms: An alternative view and frontal zones as “pelagic seed banks.” Harmful Algae. 2002;1: 95–112. doi:10.1016/S1568-9883(02)00010-0
276.
Garduño RA, Hall BD, Brown L, Robinson MG. Two Distinct Colonial Morphotypes of Amphora Coffeaeformis (bacillariophyceae) Cultured on Solid Media. J Phycol. 1996;32: 469–478. doi:10.1111/j.0022-3646.1996.00469.x
277.
Kaczmarska I, Mather L, Luddington IA, Muise F, Ehrman JM. Cryptic diversity in a cosmopolitan diatom known as Asterionellopsis Glacialis (Fragilariaceae): Implications for ecology, biogeography, and taxonomy. Am J Bot. 2014;101: 267–286. doi:10.3732/ajb.1300306
278.
Dąbek P, Ashworth MP, Górecka E, Krzywda M, Bornman TG, Sato S, et al. Toward a multigene phylogeny of the Cymatosiraceae (Bacillariophyta, Mediophyceae) II: Morphological and molecular insights into the taxonomy of the forgotten species Campylosira Africana \(<\backslash\)i\(>\) and of Extubocellulus \(<\backslash\)i\(>\), with a Description of Two New Taxa. J Phycol. 2019;55: 425–441. doi:10.1111/jpy.12831
279.
Rae BD, Long BM, Whitehead LF, Förster B, Badger MR, Price GD. Cyanobacterial Carboxysomes: Microcompartments that Facilitate CO 2 Fixation. J Mol Microbiol Biotechnol. 2013;23: 300–307. doi:10.1159/000351342
280.
Chung I-K, Kang Y-H. A Marine Picophytoplankter from Korea: Pycnococcus Provasolii Guillard. Journal of the korean society of oceanography. 1996;31: 150–154.
281.
Kvernvik AC, Rokitta SD, Leu E, Harms L, Gabrielsen TM, Rost B, et al. Higher sensitivity towards light stress and ocean acidification in an Arctic sea-ice-associated diatom compared to a pelagic diatom. New Phytologist. 2020;226: 1708–1724. doi:10.1111/nph.16501
282.
Olenina I, Hajdu S, Edler L, Andersson A, Wasmund N, Busch S, et al. Biovolumes and Size-Casses of Phytoplankton in the Baltic Sea. Baltic Marine Environment Protection Commission; 2006. Report No.: 106.
283.
Rokitta S. Transcriptome assemblies of Thalassiosira Hyalina and Nitzschia Frigida. Zenodo; 2019. doi:10.5281/zenodo.3361258
284.
Rozanska M, Gosselin M, Poulin M, Wiktor J, Michel C. Influence of environmental factors on the development of bottom ice protist communities during the winterspring transition. Mar Ecol Prog Ser. 2009;386: 43–59. doi:10.3354/meps08092
285.
Hegseth EN, Sundfjord A. Intrusion and blooming of Atlantic phytoplankton species in the high Arctic. J Mar Syst. 2008;74: 108–119. doi:10.1016/j.jmarsys.2007.11.011
286.
Webb EA, Ehrenreich IM, Brown SL, Valois FW, Waterbury JB. Phenotypic and genotypic characterization of multiple strains of the diazotrophic cyanobacterium, Crocosphaera Watsonii, isolated from the open ocean. Environmental Microbiology. 2009;11: 338–348. doi:10.1111/j.1462-2920.2008.01771.x
287.
Bench S, Ilikchyan I, Tripp H, Zehr J. Two Strains of Crocosphaera Watsonii with Highly Conserved Genomes are Distinguished by Strain-Specific Features. Front Microbiol. 2011;2: 261. doi:10.3389/fmicb.2011.00261
288.
Foster RA, Sztejrenszus S, Kuypers MMM. Measuring carbon and N2 fixation in field populations of colonial and free-living unicellular cyanobacteria using nanometer-scale secondary ion mass Spectrometry1. J Phycol. 2013;49: 502–516. doi:10.1111/jpy.12057
289.
Beer S, Björk M, Beardall J. Photosynthesis in the marine environment. Second edition. Ames, Iowa : Chichester, West Sussex, UK: John Wiley & Sons, Inc; 2014.
290.
Welsh EA, Liberton M, Stöckel J, Loh T, Elvitigala T, Wang C, et al. The genome of Cyanothece 51142, a unicellular diazotrophic cyanobacterium important in the marine nitrogen cycle. Proc Natl Acad Sci U S A. 2008;105: 15094–15099. doi:10.1073/pnas.0805418105
291.
Mareš J, Johansen JR, Hauer T, Zima Jr. J, Ventura S, Cuzman O, et al. Taxonomic resolution of the genus Cyanothece (Chroococcales, Cyanobacteria), with a treatment on Gloeothece and three new genera, Crocosphaera, Rippkaea, and Zehria. J Phycol. 2019;55: 578–610. doi:10.1111/jpy.12853
292.
Kloster M, Kauer G, Esper O, Fuchs N, Beszteri B. Morphometry of the diatom Fragilariopsis Kerguelensis from Southern Ocean sediment: High-throughput measurements show second morphotype occurring during glacials. Marine Micropaleontology. 2018;143: 70–79. doi:10.1016/j.marmicro.2018.07.002
293.
Cortese G, Gersonde R. Morphometric variability in the diatom Fragilariopsis kerguelensis: Implications for Southern Ocean paleoceanography. Earth and Planetary Science Letters. 2007;257: 526–544. doi:10.1016/j.epsl.2007.03.021
294.
Cefarelli AO, Ferrario ME, Almandoz GO, Atencio AG, Akselman R, Vernet M. Diversity of the diatom genus Fragilariopsis in the Argentine Sea and Antarctic waters: Morphology, distribution and abundance. Polar Biol. 2010;33: 1463–1484. doi:10.1007/s00300-010-0794-z
295.
Cusack C, Raine R, Patching JW. Occurrence of Species from the Genus Pseudo-nitzschia Peragallo in Irish Waters. Biology and Environment: Proceedings of the Royal Irish Academy. 2004;104B: 55–74.
296.
Rines J, Donaghay P, Dekshenieks M, Sullivan J, Twardowski M. Thin layers and camouflage: Hidden Pseudo-nitzschia Spp. (Bacillariophyceae) populations in a fjord in the San Juan Islands, Washington, USA. Mar Ecol Prog Ser. 2002;225: 123–137. doi:10.3354/meps225123
297.
Hernández-Becerril DU. Species of the planktonic diatom genus Pseudo-nitzschia of the Pacific coasts of Mexico. Hydrobiologia. 1998;379: 77–84. doi:10.1023/A:1003471828302
298.
Shetye SS, Mohan R, Patil S, Kumar A. Diatom distribution in the Enderby Basin, East Antarctica. Polar Science. 2021; 100748. doi:10.1016/j.polar.2021.100748
299.
Hasle GR, Semina HJ. The Marine Planktonic Diatoms Thalassiothrix Longissima\(<\backslash\)i\(>\) and Thalassiothrix Antarctica with Comments onThalassionema Spp. and Synedra Reinboldii. Diatom Research. 1987;2: 175–192. doi:10.1080/0269249X.1987.9704996
300.
Hasle GR. The Marine, Planktonic Diatom Family Thalassionemataceae: Morphology, Taxonomy and Distribution. Diatom Research. 2001;16: 1–82. doi:10.1080/0269249X.2001.9705509
301.
Johansson ON, Pinder MIM, Ohlsson F, Egardt J, Töpel M, Clarke AK. Friends With Benefits: Exploring the Phycosphere of the Marine Diatom Skeletonema Marinoi. Front Microbiol. 2019;10: 1828. doi:10.3389/fmicb.2019.01828
302.
Bo W, Baihui C, Qi L, Quanxi W. Morphological description of the Genus Skeletonema (Bacillariophyceae) in Yangtze River EstuaryChina. Journal of Shanghai Normal University( Natural Sciences). 2013;42: 6.
303.
Amato A, Sabatino V, Nylund GM, Bergkvist J, Basu S, Andersson MX, et al. Grazer-induced transcriptomic and metabolomic response of the chain-forming diatom Skeletonema Marinoi. ISME J. 2018;12: 1594–1604. doi:10.1038/s41396-018-0094-0
304.
Jung S-W, Yun S-M, Lee S-D, Kim Y-O, Lee J-H. Morphological Characteristics of Four Species in the Genus Skeletonema in Coastal Waters of South Korea. ALGAE. 2009;24: 195–203. doi:10.4490/ALGAE.2009.24.4.195
305.
Cheng J, Li Y, Liang J, Gao Y, Wang P, Kin-Chung H, et al. Morphological variability and genetic diversity in five species of Skeletonema (Bacillariophyta). Progress in Natural Science. 2008;18: 1345–1355. doi:10.1016/j.pnsc.2008.05.002
306.
Biquand E, Okubo N, Aihara Y, Rolland V, Hayward DC, Hatta M, et al. Acceptable symbiont cell size differs among cnidarian species and may limit symbiont diversity. The ISME Journal. 2017;11: 1702–1712. doi:10.1038/ismej.2017.17
307.
Aranda M, Li Y, Liew YJ, Baumgarten S, Simakov O, Wilson MC, et al. Genomes of coral dinoflagellate symbionts highlight evolutionary adaptations conducive to a symbiotic lifestyle. Scientific Reports. 2016;6. doi:10.1038/srep39734
308.
Lin S, Cheng S, Song B, Zhong X, Lin X, Li W, et al. The Symbiodinium Kawagutii genome illuminates dinoflagellate gene expression and coral symbiosis. Science. 2015;350: 691–694. doi:10.1126/science.aad0408
309.
Lee SY, Jeong HJ, Kang NS, Jang TY, Jang SH, Lajeunesse TC. Symbiodinium Tridacnidorum Sp. Nov., A Dinoflagellate Common to Indo-Pacific Giant Clams, and a Revised Morphological Description of Symbiodinium Microadriaticum Freudenthal, Emended Trench & Blank. European Journal of Phycology. 2015;50: 155–172. doi:10.1080/09670262.2015.1018336
310.
van Baren MJ, Bachy C, Reistetter EN, Purvine SO, Grimwood J, Sudek S, et al. Evidence-based green algal genomics reveals marine diversity and ancestral characteristics of land plants. BMC Genomics. 2016;17. doi:10.1186/s12864-016-2585-6
311.
Simon N, Foulon E, Grulois D, Six C, Desdevises Y, Latimier M, et al. Revision of the Genus Micromonas Manton et Parke (Chlorophyta, Mamiellophyceae), of the Type Species M. Pusilla (Butcher) Manton & Parke and of the Species M. Commoda van Baren, Bachy and Worden and Description of Two New Species Based on the Genetic and Phenotypic Characterization of Cultured Isolates. Protist. 2017;168: 612–635. doi:10.1016/j.protis.2017.09.002
312.
Lovejoy C. Small Planktonic single celled eukaryotes from the Arctic Ocean. 2010. doi:10.25585/1488049
313.
Figueroa R, Garcés E, Massana R, Camp J. Description, Host-specificity, and Strain Selectivity of the Dinoflagellate Parasite Parvilucifera Sinerae sp. Nov. (Perkinsozoa). Protist. 2008;159: 563–578. doi:10.1016/j.protis.2008.05.003
314.
Thomsen HA. An ultrastructural survey of the chrysophycean genus Paraphysomonas under natural conditions. British Phycological Journal. 1975;10: 113–127. doi:10.1080/00071617500650111
315.
Demura M, Noël M-H, Kasai F, Watanabe MM, Kawachi M. Taxonomic revision of Chattonella Antiqua, C. Marina and C. Ovata (Raphidophyceae) based on their morphological characteristics and genetic diversity. Phycologia. 2009;48: 518–535. doi:10.2216/08-98.1
316.
Shikata T, Takahashi F, Nishide H, Shigenobu S, Kamei Y, Sakamoto S, et al. RNA-Seq Analysis Reveals Genes Related to Photoreception, Nutrient Uptake, and Toxicity in a Noxious Red-Tide Raphidophyte Chattonella Antiqua. Front Microbiol. 2019;10. doi:10.3389/fmicb.2019.01764
317.
Horiguchi T. Heterocapsa Circularisquama sp. Nov. (Peridiniales, Dinophyceae): A new marine dinoflagellate causing mass mortality of bivalves in Japan. Phycological Research. 1995;43: 129–136. doi:10.1111/j.1440-1835.1995.tb00016.x
318.
Chepurnov VA, Mann DG, Dassow P von, Vanormelingen P, Gillard J, Inzé D, et al. In search of new tractable diatoms for experimental biology. BioEssays. 2008;30: 692–702. doi:10.1002/bies.20773
319.
Osuna-Cruz CM, Bilcke G, Vancaester E, De Decker S, Bones AM, Winge P, et al. The Seminavis Robusta genome provides insights into the evolutionary adaptations of benthic diatoms. Nat Commun. 2020;11: 3320. doi:10.1038/s41467-020-17191-8
320.
Figueroa RI, Bravo I, Fraga S, Garcés E, Llaveria G. The Life History and Cell Cycle of Kryptoperidinium Foliaceum, A Dinoflagellate with Two Eukaryotic Nuclei. Protist. 2009;160: 285–300. doi:10.1016/j.protis.2008.12.003
321.
Moldrup M, Moestrup Ø, Hansen PJ. Loss of Phototaxis and Degeneration of an Eyespot in Long-term Algal Cultures: Evidence from Ultrastructure and Behaviour in the Dinoflagellate Kryptoperidinium Foliaceum. Journal of Eukaryotic Microbiology. 2013;60: 327–334. doi:10.1111/jeu.12036
322.
Foflonker F, Price DC, Qiu H, Palenik B, Wang S, Bhattacharya D. Genome of the halotolerant green alga Picochlorum Sp. Reveals strategies for thriving under fluctuating environmental conditions. Environmental Microbiology. 2015;17: 412–426. doi:10.1111/1462-2920.12541
323.
Foflonker F, Mollegard D, Ong M, Yoon HS, Bhattacharya D. Genomic Analysis of Picochlorum Species Reveals How Microalgae May Adapt to Variable Environments. Molecular Biology and Evolution. 2018;35: 2702–2711. doi:10.1093/molbev/msy167
324.
Oliveira M. Magnetic Stimulation on the Growth of the Microalga Nannochloropsis Oculata. Master of {{Engineering Science}} Degree in {{Chemical}} and {{Biochemical Engineering}}, The University of Western Ontario. 2017.
325.
Hulatt CJ, Wijffels RH, Posewitz MC. The Genome of the Haptophyte Diacronema Lutheri (Pavlova Lutheri, Pavlovales): A Model for Lipid Biosynthesis in Eukaryotic Algae. Genome Biology and Evolution. 2021;13: evab178. doi:10.1093/gbe/evab178
326.
Hongo Y, Kimura K, Takaki Y, Yoshida Y, Baba S, Kobayashi G, et al. The genome of the diatom Chaetoceros Tenuissimus carries an ancient integrated fragment of an extant virus. Sci Rep. 2021;11: 22877. doi:10.1038/s41598-021-00565-3
327.
Füssy Z, Masařová P, Kručinská J, Esson HJ, Oborník M. Budding of the Alveolate Alga Vitrella Brassicaformis Resembles Sexual and Asexual Processes in Apicomplexan Parasites. Protist. 2017;168: 80–91. doi:10.1016/j.protis.2016.12.001
328.
Woo YH, Ansari H, Otto TD, Klinger CM, Kolisko M, Michálek J, et al. Chromerid genomes reveal the evolutionary path from photosynthetic algae to obligate intracellular parasites. eLife. 2015;4: e06974. doi:10.7554/eLife.06974
329.
Oborník M, Modrý D, Lukeš M, Černotíková-Stříbrná E, Cihlář J, Tesařová M, et al. Morphology, Ultrastructure and Life Cycle of Vitrella Brassicaformis n. Sp., N. Gen., A Novel Chromerid from the Great Barrier Reef. Protist. 2012;163: 306–323. doi:10.1016/j.protis.2011.09.001
330.
Lemieux C, Turmel M, Otis C, Pombert J-F. A streamlined and predominantly diploid genome in the tiny marine green alga Chloropicon Primus. Nat Commun. 2019;10: 4061. doi:10.1038/s41467-019-12014-x